Notes on "Artificial Intelligence in Bioscience Symposium 2017"

ArtificialIntelligenceinBioscienceSymposium
https://www.bioscience.ai/ |#bioai2017 |Sept 14,2017 | The BritishLibrary, London
PetteriTeikari,PhD
http://petteri-teikari.com/ | https://www.linkedin.com/in/petteriteikari/
Version “Sat 23 September 2017“
Noteson

Whatwassaid
Briefoverview whatwasactuallysaidatthe conference
Whatcould havebeensaid
Ifthere would have been moretime and interest for in-depth
presentations,commentsand literaturereviewshave been
gatheredaroundthediscussedtopics
Howanalyzed
Givenmyown backgroundinengineering/ visual
neurosciences/ deeplearning,the drugdiscoveryisnot
analyzedin-depth.Theaimwasrathertofindanalogiesin more
electro-optical medicineanddataminingin general
Howstructured
In adense“teaser”-fashion tryingtobrieflyshow thedirections
that interestedreaderscanmoveiftheyare interested in
investingtheirown time andlearningmore
Structureof thepresentation
Personal subjective experienceoftheconference: https://www.bioscience.ai/ #bioai2017

PhilippeSanseau
Improvingdrugtarget selection

PhilippeSanseau
Improvingdrugtarget selection#1
Torkamani et al. High-Definition Medicine
Cell Volume 170, Issue 5, 24 August 2017, Pages 828-843
https://doi.org/10.1016/j.cell.2017.08.007
The Economics of Reproducibility in Preclinical
Research Leonard P. Freedman , Iain M. Cockburn, Timothy S. Simcoe
PLOS Biology June 9 2015, https://doi.org/10.1371/journal.pbio.1002165
“Estimated US preclinical research spend and
categories of errors that contribute to
irreproducibility.” Very costly the research the
research that is irreproducible.
Target discovery and genetics evidence
Cook, David, et al. "Lessons learned from the fate of
AstraZeneca's drug pipeline: a five-dimensional
framework." Nature reviews. Drug discovery 13.6 (2014):
419. http://dx.doi.org/10.1038/nrd4309
Nelson, Matthew R., et al. "The support of human
genetic evidence for approved drug indications."
Nature genetics 47.8 (2015): 856.
http://dx.doi.org/10.1038/ng.3314
“We estimate that selecting genetically supported
targets could double the success rate in clinical
development. Therefore, using the growing wealth
of human genetic data to select the best targets
and indications should have a measurable impact
on the successful development of new drugs.”

PhilippeSanseau
http://www.targetvalidation.org/
https://www.opentargets.org/
Koscielny, Gautier, et al. "Open Targets: a platform for
therapeutic target identification and validation." Nucleic
acids research 45.D1 (2016): D985-D994.
https://dx.doi.org/10.1093/nar/gkw1055 - Cited by 14
Kafkas, Şenay, Ian Dunham, and Johanna McEntyre.
"Literature evidence in open targets-a target validation
platform." Journal of biomedical semantics8.1 (2017): 20.
https://doi.org/10.1186/s13326-017-0131-3

PhilippeSanseau
Ferrero, Enrico, Ian Dunham, and Philippe Sanseau. "In
silico prediction of novel therapeutic targets using
gene–disease association data." Journal of
translational medicine 15.1 (2017): 182.
https://doi.org/10.1186/s12967-017-1285-6
Feature importance and classification
criteria. a Feature importance according
to two independent feature selection
methods (left to right): Chi squaredtest
and information gain. b Decision tree
classification criteria: colours represent
predicted outcome (purple non-
target, green target). In each node,
numbers represent (from top to
bottom): outcome (0: non-target, 1:
target), number of observations in node
per class (left non-target, righttarget),
percentage of observations in node
Semi-supervised learning to predict novel targets
Create numeric features by taking mean score across
all diseases:
● Genetic associations (germline)
● Somatic mutations
● Significant gene expression changes
● Disease-relevant phenotype in animal model
● Pathway-level evidence
Nested cross-validation and bagging for tuning
and model selection
PU learning (Partially Supervised Classification,
Learning from Positive and Unlabeled Examples)
https://www.cs.uic.edu/~liub/NSF/PSC-IIS-0307239.html
du Plessis et al. (2014)
In other words, same as more commonly nowadays
used term semi-supervised learning

PhilippeSanseau
Literature text mining validation of predictions
using SciBite: https://www.scibite.com/
https://www.scibite.com/case-studies/case-study/biomarke
r-discovery-in-biomedical-literature/
→ https://doi.org/10.1186/2043-9113-4-13

PhilippeSanseau
Comments and further literature background: Reproducibility#1
Poor reproducibility of studiesisdetrimental for the progressof science
Need to develop R-indexor make itactuallymore popular asthere are already initiativesfor that
http://verumanalytics.io/
Sean Rife, Josh Nicholson, Yuri Lazebnik, Peter Grabitz
We propose to solve the credibility problem by
assigning each scientific report a simple
measure of veracity, the R-factor, with R
standing for reputation, reproducibility,
responsibility, and robustness
http://blogs.discovermagazine.com/neuroskepti
c/2017/08/21/r-factor-fix-science/#.Wb0mVZ_6x
hH
Science with no fiction: measuring the veracity of
scientific reports by citation analysis
Peter Grabitz, Yuri Lazebnik, Joshua Nicholson, Sean Rife
http://www.biorxiv.org/content/early/2017/08/09/172940
https://doi.org/10.1101/172940

PhilippeSanseau
Comments and further literature background: Reproducibility#2 with Blockchain
Towards a scientific
blockchain framework for
reproducible data analysis
C. Furlanello, M. De Domenico, G. Jurman,
N. Bussola (Submitted on 20 Jul 2017)
https://arxiv.org/abs/1707.06552
Our mechanism builds a trustless ecosystem
of researchers, funding bodies and publishers
cooperating to guarantee digital and permanent
access to information and reproducible results.
As a natural byproduct, a procedure to
quantify scientists' and institutions' reputation
for ranking purposes is obtained.
Decentralized electronic health records (EHR, EMR)
reducethe powerofthecronycapitalist EPICsofthe world.
→ more efficient and cost-effectivesystems
→ dataminingfordata-drivenmedicinegetsalot easier
AI in Ophthalmology | Startup Landscape
Petteri Teikari, PhD
Published on Aug 19, 2016
https://www.slideshare.net/PetteriTeikariPhD/artificial-intellig
ence-in-ophthalmology

PhilippeSanseau
Comments and further literature background: Reproducibility#3 with Blockchain
Who Will Build the Health-Care
Blockchain? Decentralized databases
promise to revolutionize medical records, but not
until the health-care industry buys in to the idea
andgetstowork.
By Mike Orcutt, September 15, 2017
https://www.technologyreview.com/s/608821/who-will-build-the-health-care-blockchain/
There are 26 different electronic medical records systems used in the city of
Boston, each with its own language for representing and sharing data. Critical information is
often scattered across multiple facilities, and sometimes it isn’t accessible when it is needed
most—a situation that plays out every day around the U.S., costing money and sometimes
even lives. But it’s also a problem that looks tailor-made for a blockchain to solve, says
JohnHalamka,chief informationofficer atBeth Israel DeaconessMedical CenterinBoston.
EmilyVaughn, head of accountsat Gem, astartup thathelpscompanies adoptblockchain
technology, says that’s only just starting to be worked out. “There may be specific rules we
want to bake into the protocol to make it better for health care,” she says. The system must
facilitate the exchange of complex health information between patients and providers,
for example, as well as exchanges between providers, and between providers and
payers—all while remaining secure from malicious attacks and complying with privacy
regulations.
The best way to do all that is still far from clear. But Halamka and researchers at the MIT
Media Lab have developed a prototype system called MedRec (pdf), using a private
blockchain based on Ethereum. It automatically keeps track of who has permission to view
and changea record of medicationsa personistaking.
Either way, blockchain’s potential for the health-care industry depends on whether
hospitals, clinics, and other organizations are willing to help create the technical infrastructure
required. To that end, Gem is working with clients to prototype a global, blockchain-based
patient identifier thatcould belinked to hospital recordsaswell asdata fromother sourceslike
employee wellness programs and wearable health monitors. It could be just the
thing to sewtogether themaddening patchwork of digital systemsavailablenow.
BUSINESS GUEST
How blockchain will finally convert you: Control over your own data
BEN DICKSON, TECHTALKS@BENDEE983 SEPTEMBER 9, 2017 12:10 PM
And then there are cases like the massive data breach Equifax reported this week, where 143 million
consumers’ social security numbers, addresses, and other data was exposed to hackers and identity thieves.
This is where blockchain and distributed ledgers promise consumers real value. Blockchain’s architecture
enables user data to be siloed from the server applications that use it. A handful of companies are exploring the
concept to put users back in control of their data.
Pillar, another open-source blockchain project, is
developing what it calls a personal data locker
and “smart wallet.” Pillar is a mobile app that stores
and manages your digital assets on the blockchain,
where you have full ownership and control. These
assets can be cryptocurrencies, health records,
contact information, documents, and more. Pillar
also aims to address another fundamental
problem: The average consumer’s lack of
interest in managing their own data.
Projects such as Enigma employ blockchain to
preserve user data privacy while sharing it with
cloud services and third parties. Enigma’s platform
protects data by encrypting it, splitting it into
several pieces and randomly distributing those
indecipherable chunks across multiple nodes in
its network. Enigma uses “secure multiparty
computation” for its operations: Each node
performs calculations on its individual chunk of
data and returns the result to the user, who can
then combine it with others to assemble the final
result.
For “The EU General Data Protection Regulation (GDPR)”

PhilippeSanseau
Comments and further literature background: Reproducibility#4 Datasets withTorrent
Making 22.41TB of research data available!
http://academictorrents.com/
We've designed a distributed system for sharing enormous
datasets - for researchers, by researchers. The result is a scalable,
secure, and fault-tolerant repository for data, with blazing fast
download speeds. Contact us at contact@academictorrents.com.
Accelerate your hosting for free with our academic BitTorrent
infrastructure!
+ One aim of this site is to create the infrastructure to allow open access journals to operate at low
cost. By facilitating file transfers, the journal can focus on its core mission of providing world class research.
Afterpeerreviewthepapercanbeindexedon thissiteanddisseminatedthroughoutoursystem.
+ Large dataset delivery can be supported by researchers in thefield that have the dataset on their machine.
A popular large dataset doesn't need to be housed centrally. Researchers can have part of the
datasettheyareworkingon andtheycan helphostittogether.
+ Libraries can host this data to host papers from their own campus without becoming the only
sourceofthedata. Soeven if alibrary'ssystemisbroken otheruniversitiescan participatein gettingthat data
into thehandsofresearchers.

WinstonHide
Could Machine LearningEver Cure Alzheimer’s Disease?

WinstonHide
Could Machine LearningEver Cure Alzheimer’s Disease? #1
Machinelearnists Mantra
● Traceability
● Interpretability
● Reproducability
● Validatable
Barbara Engelhardt – Latent factor models
Prof. Engelhardt is a PI in the Genotype-Tissue Expression
(GTEx) consortium.
Engelhardt, Barbara E., and Matthew Stephens. "Analysis of population
structure: a unifying framework and novel methods based on
sparse factor analysis." PLoS genetics 6.9 (2010): e1001117.
https://doi.org/10.1371/journal.pgen.1001117
Pathways outperform genes [Genome wide
association studies (GWAS) ] as classifiers
Holly F. Ainsworth et al. (2017): The use of
causal inference techniques to integrate
omics and GWAS data has the potential to
improve biological understanding of the
pathways leading to disease. Our study
demonstrates the suitability of various
methods for performing causal inference
under several biologically plausible scenarios.
KEYWORDS Bayesian networks, causal inference, Mendelian
randomisation, structural equation modelling
Pathprint robust to batch effect and allows
compraison of gene expression at the pathway
level across multiple array platforms
Altschuler G, Hofmann O, Kalatskaya I, Payne R, Ho Sui SJ, Saxena U, Krivtsov AV,
Armstrong SA, Cai T, Stein L and Hide WA (2013). “Pathprinting: An integrative
approach to understand the functional basis of disease.” Genome Med, pp. 68–
81. Bioconductor: https://doi.org/doi:10.18129/B9.bioc.pathprint | Cited by 6 articles
Taking Bioinformatics to Systems Medicine
Antoine H. C. van Kampen, Perry D. Moerland
https://doi.org/10.1007/978-1-4939-3283-2_2

WinstonHide
How to pathways relate to each other?
● Geneset relate to other curated and data
derived genesets / pathways?
● Experimental signature on a high level map of
cellular function
● Core pathways driving a phenotype?
● Relationship with Genetic/genome upstream
perturbation and the functional phenotype?
Understanding the relative role of a function
● Gene set enrichment – edges of the graph represent mutual overlap
Isserlin, Ruth, et al. "Enrichment Map–a
Cytoscape app to visualize and explore
OMICs pathway enrichment
results." F1000Research 3 (2014).
https://dx.doi.org/10.12688/f1000research.4536.1
Felgueiras, Juliana, Joana Vieira Silva, and Margarida
Fardilha. "Adding biological meaning to human
protein-protein interactions identified by yeast two-
hybrid screenings: A guide through bioinformatics
tools." Journal of Proteomics (2017).
https://doi.org/10.1016/j.jprot.2017.05.012
Michaut, Magali, et al. "Integration of genomic,
transcriptomic and proteomic data identifies two
biologically distinct subtypes of invasive lobular
breast cancer." Scientific reports 6 (2016): 18517.
http://doi.org/10.1038/srep18517
Cited by 23
Maia, Ana-Teresa, et al. "Big data in cancer
genomics." Current Opinion in Systems Biology 4
(2017): 78-84. https://doi.org/10.1016/j.coisb.2017.07.007

WinstonHide
→ Pathway Coexpression Network Reproducibility “Biologists are likely to find that larger studies turn up more and more
genetic variants – or “hits” - that have minuscule influences on
disease” - Jonathan Pritchard, Stanford University
Gaps in understanding about biochemical networks.
“We might not actually be learning anything hugely interesting until
we understand how these networks are connected”
- Joe Pickrell, New York Genome Center
New concerns
raised over value of
genome-wide
disease studies
Nature
10.1038/nature.2017.22152
Ewen Callaway 15
June 2017
https://doi.org/10.1016/j.jbi.2009.09.005
https://doi.org/10.1093/nar/gkw797
http://dx.doi.org/10.1126/science.1087447
http://dx.doi.org/10.1111/gbb.12106

WinstonHide
Sheffield Institute for Translational Neuroscience
Harvard School of Public Health (Yered Hammurabi Pita-
Juarez and Les Kobzik)
Massachusetts Institute of Technology (Manolis Kellis)
Cure Alzheimer’s Fund (Rudy Tanzi)
Centre for Genome Translation (Gabriel Altschuler, Vivien
Junker, Wenbin Wei, Sarah Morgan, Katjuša Koler, Sandeep
Amberkar, David Jones, Sokratis Kariotis, Claira Green)
Winston hide (@winhide) | Twitter
https://hidelab.wordpress.com/
Small world property of gene networks most expressed disease
associated genes are only a few steps from the nearest core gene
Gaiteri and Sibille (2011)
https://doi.org/10.3389/fnins.2011.00095
Schematic of
relationship
between network
structure and
differential
expression
incorporating all
results.

WinstonHide
Comments and further literature background: Graphsfor understanding genes #1
Geometric Deep Learning
https://www.slideshare.net/PetteriTeikariPhD/geometric-deep-learning
How Different Are Estimated Genetic
Networks of Cancer Subtypes?
Ali Shojaie, Nafiseh Sedaghat
22 March 2017 | Big and Complex Data Analysis pp 159-192
https://doi.org/10.1007/978-3-319-41573-4_9
Genomic analysis of regulatory network
dynamics reveals large topological changes
Nicholas M. Luscombe, M. Madan Babu, Haiyuan Yu, Michael Snyder, Sarah A.
Teichmann & Mark Gerstein
Nature 431, 308-312 (16 September 2004)
http://dx.doi.org/10.1038/nature02782
http://doi.org/10.1126/science.298.5594.824

WinstonHide
Comments and further literature background: ‘Networkbiology’#1A
:Functional connectome
Functional connectome fingerprinting: identifying
individuals using patterns of brain connectivity
Emily S Finn, Xilin Shen, Dustin Scheinost, Monica D Rosenberg,
Jessica Huang, Marvin M Chun, Xenophon Papademetris & R Todd Constable
Nature Neuroscience 18, 1664–1671 (2015) doi: 10.1038/nn.4135
The dynamic functional connectome: State-of-the-art and
perspectives
Maria Giulia Preti, Thomas AW Bolton, Dimitri Van De Ville
NeuroImage (Available online 26 December 2016)
https://doi.org/10.1016/j.neuroimage.2016.12.061
Functional connectivity dynamically evolves on multiple
time-scales over a static structural connectome: Models
and mechanisms
Joana Cabral, Morten L. Kringelbach, Gustavo Deco
NeuroImage (Available online 23 March 2017)
Connectome imaging for mapping human brain pathways
Y Shi and A W Toga
Molecular Psychiatry (2017) 22, 1230–1240; doi: 10.1038/mp.2017.92
“Using connectome imaging, we have the opportunity to develop robust
algorithms and software tools to systematically characterize the integrity
of these circuits. In addition to in-depth modeling and quantification of
these brain circuits, connectome-based parcellation will produce whole-
brain network models at much finer resolution than existing works.
Together with multimodal fusion strategies, these connectome features
will form a set of deep phenotypes for mining with genetic and
behavioral data. This matches perfectly with current developments in Big
Data and deep learning methods.”
Structural vs functional connectivity. (Left) Advanced tractography algorithms allow reconstructing the
white matter fiber tracts from Diffusion-MRI. The structural connectivity matrix SC(n,p) is estimated in
proportion to the number of fiber tracts detected between any two brain areas n and p. (Right) On the other
hand, the functional connectivity matrix FC(n,p) is computed as the correlation between the brain activity (e.g.
BOLD signal) estimated in areas n and p over the whole recording time. Here, the areas refer to 90 non-
cerebellar brain areas from the AAL template. - Cabral et al. (2017)

WinstonHide
Comments and further literature background: ‘Networkbiology’#1B
: Functional connectome
Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity
Danielle S. Bassett, Ankit N. Khambhati, and Scott T. Grafton
Annual Review of Biomedical Engineering Vol. 19:327-352 (Volume publication date June 2017)
https://doi.org/10.1146/annurev-bioeng-071516-044511
Neuroengineering is faced with unique challenges
in repairing or replacing complex neural systems
that are composed of many interacting parts.
These interactions form intricate patterns over
large spatiotemporal scales and produce
emergent behaviors that are difficult to predict
from individual elements. Network science
provides a particularly appropriate framework in
which to study and intervene in such systems by
treating neural elements (cells, volumes) as
nodes in a graph and neural interactions
(synapses, white matter tracts) as edges in that
graph. Here, we review the emerging discipline of
network neuroscience, which uses and
develops tools from graph theory to better
understand and manipulate neural systems from
micro- to macroscales. We present examples of
how human MRI brain imaging data (or EEG, MEG,
ECOG, fNIRS, etc.) are being modeled with
network analysis and underscore potential
pitfalls. We then highlight current computational
and theoretical frontiers and emphasize their
utility in informing diagnosis and monitoring,
brain–machine interfaces, and brain stimulation.
A flexible and rapidly evolving enterprise, network
neuroscience provides a set of powerful
approaches and fundamental insights that are
critical for the neuroengineer's tool kit.
Multiscale topology in brain networks. Brain
networks express fundamental organizing
principles across multiple spatial scales. Brain
networks are modeled as a collection of nodes
(representing regions of interest with
presumably coherent functional
responsibilities) and edges (structural
connections or functional interactions between
brain regions). Constructing connectomes from magnetic resonance imaging (MRI) data. To
generate human connectomes with MRI, an anatomic scan delineating gray matter is
partitioned into a set of nodes. This scan is combined with either diffusion scans of
white matter structural connections or time series of brain activity measured by
functional MRI, resulting in a weighted connectivity matrix.

WinstonHide
Comments and further literature background: ‘Networkbiology’#1C
:Functionalconnectome
Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity
Danielle S. Bassett, Ankit N. Khambhati, and Scott T. Grafton
Annual Review of Biomedical Engineering Vol. 19:327-352 (Volume publication date June 2017)
https://doi.org/10.1146/annurev-bioeng-071516-044511
Tools for higher-order interactions from algebraic topology.
(a) The human connectome is a complex network architecture
that contains both dyadic and higher-order interactions. Graph
representations of the human connectome encode only dyadic
relationships, leaving higher-order interactions unaccounted for. A
natural way in which to encode higher-order interactions is in the
language of algebraic topology, which defines building blocks
called simplices (Giusti et al. 2016): A 0-simplex is a node, a 1-
simplex is an edge between two nodes, a 2-simplex is a filled
triangle, and so on.
Brain network regulation and control can help navigate dynamical
states. To accomplish behavioral and cognitive goals, brain networks
internally navigate a complex space of dynamical states. Putative brain
states may be situated in various peaks and troughs of an energy
landscape, requiring the brain to expend metabolic energy to move
from the current state to the next state. Within the space of possible
dynamical states, there are easily accessible states and harder-to-reach
states; in some cases, the accessible states are healthy, whereas in other
cases, they may contribute to dysfunction, and similarly for the harder-
to-reach states. Two commonly observed control strategies used by
brain networks are average control and modal control. In average
control, highly central nodes navigate the brain towards easy-to-reach
states. In contrast, modal control nodes tend to be isolated brain regions
that navigate the brain toward hard-to-reach states that may require
additional energy expenditure (Gu et al. 2015). As a self-regulation
mechanism for preventing transitions towards damaging states, the brain
may employ cooperative and antagonistic push–pull strategies (
Khambhati et al. 2016). In such a framework, the propensity for the brain
to transition toward a damaging state might be competitively limited by
opposing modal and average controllers whose goal would be to pull the
brain toward less damaging states.
Network control theory offers a powerful tool set for neuroengineers
concerns how to exogenously control a neural system and accurately
predict the outcome on neurophysiological dynamics—and, by extension,
cognition and behavior. Indeed, how to target, tune, and optimize stimulation
interventions is one of the most pressing challenges in the treatment of
Parkinson disease and epilepsy, for example (Johnson et al.2013). More
broadly, this question directly affects the targeting of optogenetic
stimulation in animals (Ching et al. 2013) and the use of invasive and
noninvasive stimulation in humans (e.g., deep brain, grid, transcranial
magnetic, transcranial direct current, and transcranial alternating current
stimulation)(Muldoon et al.2016).
Clinical translation of network neuroscience tools. Network
neuroscience offers a natural framework for improving tools to diagnose
and treat brain network disorders (e.g. epilepsy). … Functional connectivity
patterns demonstrate strong interactions around the brain regions in which
seizures begin and weak projections to the brain regions in which seizures
spread. Objective tools in network neuroscience can usher in an era of
personalized algorithms capable of mapping epileptic network
architecture from neural signals and pinpointing implantable
neurostimulation devices to specific brain regions for intervention (,
Khambhati et al. 2016,2015, Muldoon et al.2016)

WinstonHide
Comments and further literature background: ‘Networkbiology’#1D
:Functional connectome
Connectivity Inference from Neural Recording Data: Challenges, Mathematical Bases and Research Directions
Ildefons Magrans de Abril, Junichiro Yoshimoto, Kenji Doya (Submitted on 6 Aug 2017) https://arxiv.org/abs/1708.01888
Connectivity inference itself is an
interesting and deep mathematical
problem, but the goal of connectivity
inference isnotonly to precisely estimate
the connection weight matrix, but also to
illustrate how neural circuits realize
specific functions, such as sensory
inference, motor control, and decision
making.
If we can perfectly estimate network
connections from anatomical and
activitydata,then computer simulation of
the network model should be able to
reproduce the function of the
network. But given inevitable
uncertainties in connectivity inference,
reconstruction of function in a
purely data-driven way might be
difficult. How to extract or infer a
functional or computational network
from a data-driven network, or even to
combine known functional constraints
as a prior for connectivity inference, is a
possibledirectionoffutureresearch.

WinstonHide
Comments and further literature background: ‘Networkbiology’#2:Brain andGenes
Inter-regional ECoG correlations predicted by communication
dynamics, geometry, and correlated gene expression
Richard F. Betzel, John D. Medaglia, Ari E. Kahn, Jonathan Soffer, Daniel R. Schonhaut,
Danielle S. Bassett (Submitted on 19 Jun 2017)
Our models accurately predict out-of-sample electrocorticography (ECoG) networks and perform well even when fit to data from
individual subjects, suggesting shared organizing principles across persons. In addition, we identify a set of genes whose brain-
wide co-expression is highly correlated with ECoG network organization. Using gene ontology analysis, we show that these same
genes are enriched for membrane and ion channel maintenance and function, suggesting a molecular underpinning of ECoG
connectivity. Our findings provide fundamental understanding of the factors that influence interregional ECoG networks, and open the
possibility for predictive modeling of surgical outcomes in disease.

WinstonHide
Commentsandfurther literaturebackground:‘NetworkScienceModelling’#1
Modelling And Interpreting Network Dynamics
Ankit N Khambhati, Ann E Sizemore, Richard F Betzel, Danielle S Bassett
bioRxiv https://doi.org/10.1101/124016
Pharmacologic modulation of network dynamics. (A) By
blocking or enhancing neurotransmitter release through
pharmacologic manipulation, investigators can perturb the
dynamics of brain activity. For example, an NMDA receptor agonist
might hyper-excite brain activity, while a NMDA receptor antagonist
might reduce levels of brain activity. (B) Hypothetically speaking, by
exogenously modulating levels of a neurotransmitter, one might be
able to titrate the dynamics of brain activity and the accompanying
functional connectivity to avoid potentially damaging brain states.

WinstonHide
Commentsandfurther literaturebackground:‘NetworkScienceModelling’#2A
:MultilayerNetworks
Isomorphisms in Multilayer Networks
Mikko Kivelä and Mason A. Porter. Oxford Centre for Industrial and Applied Mathematics,
last revised 16 Feb 2017
We reduce each of the multilayer network isomorphism problems to a graph
isomorphism problem, where the size of the graph isomorphism problem grows
linearly with the size of the multilayer network isomorphism problem. One can thus
use software that has been developed to solve graph isomorphism problems as a
practical means for solving multilayer network isomorphism problems. Our theory
lays a foundation for extending many network analysis methods --- including motifs,
graphlets, structural roles, and network alignment --- to any multilayer network.
Perhaps the most exciting direction in research on multilayer networks is the development of methods and models that are not
direct generalizations of any of the traditional methods and models for ordinary graphs [Kivelä et al. 2014]. The fact that there
are multiple types of isomorphisms opens up the possibility to help develop such methodology by comparing different
types of isomorphism classes. We also believe that there will be an increasing need for the study of networks that have
multiple aspects (e.g., both time-dependence and multiplexity), and our isomorphism framework is ready to be used for
such networks.
Efforts aimed at understanding and integrating the study of
social and brain network dynamics will advance
understanding of basic psychological principles and aid in
deriving fundamental principles about the organization of
society. However, even beyond fundamental knowledge,
work at this intersection has the potential to improve real-
world practice in clinical treatments for mental and
physical disorders, predicting behavior change in
response to persuasive messages, and improving
educational outcomes including learning and creativity.
For example, if people whose brain and/or social networks
show differential response to treatments, logged
information (e.g., from social media) could aid in providing
tailored interventions
Brain and Social Networks: Fundamental Building
Blocks of Human Experience
Emily B. Falk, Danielle S. BassettUniversity of Pennsylvania
Trends in Cognitive Sciences Volume 21, Issue 9, September 2017, Pages 674-690
https://doi.org/10.1016/j.tics.2017.06.009

WinstonHide
Commentsandfurther literaturebackground:‘NetworkScienceModelling’#2B
:Multilayer Networks
Multilayer Brain Networks
Michael Vaiana, Sarah Muldoon
(Submitted on 7 Sep 2017)
Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework
into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the
use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data.
Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a
modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-
world systems.
a. A cartoon illustrating how
glia serve to distribute
resources neural synapses. b.
A simplified graph
representing the two layer
glia-neuron network model.
Despite the utility of multilayer networks, to date, there are relatively few
neuroscientific studies that incorporate the multilayer framework. It will
be important for future research to utilize the ever expanding knowledge
base and set of measures for multilayer networks as well as drive
development of measures with improved sensitivity and specificity for the
many potential applications. The multilayer network framework has the
potential to become the prominent mode of network analysis in the future,
as neuroscientists face increasingly multi-modal, multi-temporal, or multi-
scale data. Multilayer network science is in its infancy and comprehensive
research into the structure and function of brain networks will be necessary
as both multilayer networks and neuroscience develop in tandem.

WinstonHide
Commentsandfurther literaturebackground:‘NetworkScienceModelling’#3:DynamicConnectivity
Dynamic Graph Metrics: Tutorial, Toolbox, and Tale
Ann E. Sizemore, Danielle S. Bassett University of Pennsylvania
(Submitted on 30 Mar 2017)
Visualizations of dynamic
networks. (a) Stacked static network
representation of a dynamic network
on ten nodes. (b) Time-aggregated
graph of dynamic network in (a). Any
two nodes that are connected at any
time in (a) are connected in this graph.
(c) Visualization of network in (a) as
contacts across time. (d) Dynamic
network of one individual during a
motor learning task. Green regions
correspond to a functional module
composed of motor areas, blue
regions correspond to a functional
module composed of visual regions,
and red regions correspond to areas
that were not in either the motor or
visual module
Time respecting paths. (a) (Left) Time
aggregated network from Fig. 1b with green and
blue paths highlighted. (Right) Contact
sequence plot from Fig. 1c with green and blue
paths highlighted. (b) The source set of the
peach node indicated with a peach ring. (c)
Composition of the source set of nodes from
the visual (left) and motor (right) modules of our
example empirical fMRI data set, depicted
across time. The gray line indicates the fraction
of all nodes in the source set, while the blue and
green lines represent the fraction of the visual
and motor nodes within the source set,
respectively. (d) Illustration of the set of
influence (t−8) of the gold node. Nodes within
this set indicated with a gold ring at the time at
which they can first be reached by the gold
node. (e) Composition of the set of influence
calculated from nodes within the visual (left)
and motor (right) groups. As in (c), the fraction
of all regions (gray), visual regions (blue), and
motor regions (green) are plotted against time.
Solid lines in (c) and (e) mark the average over
subjects and trials, and shaded regions
represent two standard deviations from this
average.

WinstonHide
Commentsandfurther literaturebackground:‘Perturbing NeuralNetworks’#1
Control of Dynamics in Brain Networks
Evelyn Tang, Danielle S. Bassett
(Submitted on 6 Jan 2017 (v1), last revised 19 Jan 2017 (this version, v2))
Model for adaptive cognitive
control showing distinct
mechanisms between different
brain regions. Schematic of a neural
network connecting the prefrontal
cortex, which executes much of the
“top-down” control actions, to other
brain regions. Another part of the
brain – the anterior cingulate cortex –
serves as a conflict monitoring
mechanism that modulates the
activity of control representations,
while an adaptive gating mechanism
regulates the updating of control
representations in prefrontal cortex
through dopaminergic projections.
Controllability metrics are positively correlated with age, with older
youth displaying greater average and modal controllability than younger
youth. Each data point represents the average strength of controllability
metrics calculated on the brain network of a single individual, in a cohort of 882
healthy youth from ages 8 to 22. Brain networks were found to be optimized to
support energetically easy transitions (average controllability) as well as
energetically costly ones (modal controllability). The color bar denotes the age
of the subjects, illustrating a significant correlation between age and the ability to
support this diverse range of dynamics (Tang et al., 2016).

WinstonHide
Topological Principles of Control in Dynamical
Network Systems
Jason Kim, Jonathan M. Soffer, Ari E. Kahn, Jean M. Vettel, Fabio Pasqualetti, Danielle S. Bassett
(Submitted on 1 Feb 2017 (v1), last revised 6 Feb 2017 (this version, v2))
Network Control of the Drosophila, Mouse, and Human Connectomes.
(a) A representation of the mouse brain via the Allen Mouse Brain Atlas, with a
superimposed simplified network. Each brain region is represented as a
vertex, and the connections between regions are represented as directed
edges.
The Simplified Network Representation Offers a Reasonable
Prediction for the Full Network’s Control Energy. (a) Graphical
representation of a non-simplified network of N drivers (red) and M
non-drivers (blue), with directed connections between all nodes
present.
Energetically Favorable Organization of Topological
Features in Networks
To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently
reconstructed from drosophila, mouse, and human brains. We use these principles to show that connectomes of
increasingly complex species are wired to reduce control energy. We then use the analytical expressions we
derive to perform targeted manipulation of the brain’s control profile by removing single edges in the
network, a manipulation that is accessible to current clinical techniques in patients with neurological
disorders. Cross-species comparisons suggest an advantage of the human brain in supporting diverse network
dynamics with small energetic costs, while remaining unexpectedly robust to perturbations. Generally, our
results ground the expectation of a system’s dynamical behavior in its network architecture, and directly inspire
new directions in network analysis and design via distributed control.

WinstonHide
Mind Control as a Guide for the Mind
John D. Medaglia, Perry Zurn, Walter Sinnott-Armstrong, Danielle S. Bassett
(Submitted on 13 Oct 2016 (v1), last revised 25 Apr 2017 (this version, v2))
https://arxiv.org/abs/1610.04134v2
A block diagram of a PID controller
The ethics of brain control As efforts to guide complex brain processes advance, we will not only need new theoretical and
technical tools. We will also face new societal, legal, and ethical challenges. Our best chance of meeting those challenges is
through ongoing, rigorous discussion between scientists, ethicists, and policy makers. Rethinking human persons As mind
control develops, the ability to interact intelligently with human nature may bring certain stakes into sharper focus. Humans
privilege the notion of a “mind” and perceived internal locus of control as central to their identities [Wilson andLenart2014].
Further, within minds, humans privilege some traits, such as social comfort, honesty, kindness, empathy, and fairness, as more
fundamental than functions, such as concentration, wakefulness, and memory [Riisetal.2008]. These different values depend
on the notion of conscious identity and are often at the core of common ethical distinctions applied to humans versus other
animals [Olson 1999]. Importantly, modern notions of human persons, influenced by continuing advances in the cognitive and
brain sciences, erode the classical boundary between the ethical treatment of humans and animals [Singer2011]. … For this
reason,scientists,clinicians,ethicists,andphilosophers willneedto work together.

WinstonHide
Comments and further literature background: ClinicaluseforNetworkAnalysis#1
Modern network science of neurological disorders
Cornelis J. Stam
Nature Reviews Neuroscience 2014
http://dx.doi.org/10.1038/nrn3801
Recent developments in the application of network science to conditions
such as Alzheimer’s disease, multiple sclerosis, traumatic brain injury and
epilepsy have challenged the classical concept of neurological disorders
being either ‘local’ or ‘global’, and have pointed to the overload and failure of
hubs as a possible final common pathway in neurological disorders.
Clinical implications of omics and systems medicine:
focus on predictive and individualized treatment
Mikael Benson
Journal of Internal Medicine (2105)
http://dx.doi.org/10.1038/nrn3801

WinstonHide
Comments and further literature background: ClinicaluseforNetworkAnalysis#2
Challenges and opportunities for system biology
standards and tools in medical research
König, M., Oellrich, A., Waltemath, D., Dobson, R. J. B., Hubbard, T. J. P., & Wolkenhauer, O.
In Proceedings of the 7th Workshop on Ontologies and Data in Life Sciences, organized by the GI Workgroup Ontologies in Biomedicine
and Life Sciences. (Vol. 1692). CEUR-WS.
https://kclpure.kcl.ac.uk/portal/files/59024860/final_submission_odls_2016.pdf
Illustration of the integration process of computational models and data from different sources.
The integration strongly relies on the availability and detail of the ontologies used for the semantic
annotations. User interfaces need to provide access to the simulation modules, but restrict the change
of parameters to ranges that are safe w.r.t. a clinical application. SBML and CellML are standards used
to encode models in a computable format. Electronic Health Records (EHRs) refers to any data
recorded in a hospital or GP practice.
Network Medicine: Complex Systems in Human
Disease and Therapeutics 23 Feb 2017
Joseph Loscalzo (Author), Albert-lászló Barabási (Author),
Edwin K. Silverman (Author), Elliott M. Antman (Author),
Michael E. Calderwood (Author)
https://www.amazon.co.uk/Network-Medicine-Complex-Systems-Therapeutics/dp/0674436539/ref=sr
_1_3?s=books&ie=UTF8&qid=1505672639&sr=1-3
Big data, genomics, and quantitative approaches to network-based
analysis are combining to advance the frontiers of medicine as never
before.Network Medicineintroduces this rapidly evolving field of medical
research, which promises to revolutionise the diagnosis and treatment of
human diseases.
Medical researchers have long sought to identify single molecular defects that cause diseases, with
the goal of developing silver-bullet therapies to treat them. But this paradigm overlooks the
inherent complexity of human diseases and has often led to treatments that are inadequate or
fraught with adverse side effects. Rather than trying to force disease pathogenesis into a
reductionist model, network medicine embraces the complexity of multiple influences on
disease and relies on many different types of networks: from the cellular-molecular level of protein-
protein interactions to correlational studies of gene expression in biological samples. The authors
offer a systematic approach to understanding complex diseases while explaining network
medicine’s unique features, including the application of modern genomics technologies,
biostatistics and bioinformatics, and dynamic systems analysis of complex molecular networks in
an integrative context.
Next generation of network medicine:
interdisciplinary signaling approaches
Tamas Korcsmaros, Maria Victoria Schneiderand Giulio Superti-Furga
DOI: 10.1039/C6IB00215C (Review Article) Integr. Biol., 2017, 9, 97-108
Precision Psychiatry Meets Network Medicine
Network Psychiatry
David Silbersweig, MD; Joseph Loscalzo, MD, PhD
JAMA Psychiatry. 2017;74(7):665-666.
doi :10.1001/jamapsychiatry.2017.0580
Network medicine: a new paradigm for
cardiovascular disease research and beyond
Jörg Menche
Cardiovascular Research, Volume 113, Issue 10, 1 August 2017,
Pages e29–e30, https://doi.org/10.1093/cvr/cvx129
Interactome-based approaches to human disease
Michael Caldera, Pisanu Buphamalai, Felix Müller, Jörg Menche
Current Opinion in Systems Biology Volume 3, June 2017, Pages 88-94
https://doi.org/10.1016/j.coisb.2017.04.015

WinstonHide
Comments and further literature background: ’NetworkDeepLearning’ #1
Deep Learning Architecture with Dynamically
Programmed Layers for Brain Connectome Prediction
Vivek Veeriah, Rohit Durvasula, Guo-Jun Qi University of Central Florida, Orlando, FL, USA
KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, Pages 1205-1214
https://doi.org/10.1145/2783258.2783399
Identifying Connectivity Patterns for Brain Diseases
via Multi-side-view Guided Deep Architectures
Jingyuan Zhang, Bokai Cao, Sihong Xie, Chun-Ta Lu, Philip S. Yu, Ann B. Ragin
Proceedings of the 2016 SIAM International Conference on Data Mining
https://doi.org/10.1137/1.9781611974348.5
In this paper, we present a novel Multi-side-View guided AutoEncoder (MVAE)
that incorporates multiple side views into the process of deep learning to tackle the
bias in the construction of connectivity patterns caused by the scarce clinical
data. Extensive experiments show that MVAE not only captures discriminative
connectivity patterns for classification, but also discovers meaningful information
for clinical interpretation.
There are several interesting directions for future work. Since brain connectomes and
neuroimages can provide complementary information for brain diseases, one interesting
direction of our future work is to explore both brain connectomes and neuroimages in
deep learning (i.e. multimodal models). Another potential direction is to combine fMRI and
DTI brain connectomes together, because the functional and structural connections
togethercan providerichinformation forlearningdeepfeaturerepresentations.

WinstonHide
Comments and further literature background: ’NetworkDeepLearning’ #2A
Multi-view Graph Embedding with Hub Detection for
Brain Network Analysis
Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin
(Submitted on 12 Sep 2017)
In this paper, we present MVGE-HD, an auto-weighted framework of Multi-view
Graph Embedding with Hub Detection for brain network analysis. We
incorporate the hub detection task into the multi-view graph embedding
framework so that the two tasks could benefit each other. The MVGE-HD
framework learns a unified graph embedding across all the views while
reducing the potential influence of the hubs on blurring the boundaries between
node clusters in the graph, thus leading to a clear and discriminative node
clustering structure for the graph. The extensive experimental results on two
real multi-view brain network datasets (i.e., HIV and Bipolar disorder)
demonstrate the effectiveness and the superior performance of the proposed
framework for brain network analysis.
Identifying Deep Contrasting Networks from Time
Series Data: Application to Brain Network Analysis
John Boaz Lee, Xiangnan Kong, Yihan Bao, Constance Moore
https://doi.org/10.1137/1.9781611974973.61
We propose a method called GCC (Graph
Construction CNN) which is based on deep
convolutional neural networks for the task of
network construction. The CNN in our model
learnsanonlinear edge-weightingfunction toassign
discriminative values to the edges of a network We
also demonstrate the extensibility of our proposed
framework by combining it with an autoencoder
to capture subgraph patterns from the
constructed networks.

WinstonHide
Comments and further literature background: ’NetworkDeepLearning’ #2B
Unsupervised Feature Extraction by Time-
Contrastive Learning and Nonlinear ICA
Aapo Hyvärinen and Hiroshi Morioka
(Submitted on 20 May 2016)
Methods such as noise-contrastive estimation [Gutmann and Hyvärinen2012] and generative
adversarial nets [Goodfellow etal. 2014], see also [Gutmann et al. 2014], are similar in spirit, but clearly
distinct from TCL which uses the temporal structure of the data by contrasting different time segments.
In practice, the feature extractor needs to be capable of approximating a general nonlinear relationship
between thedatapointsand the log-oddsof the classes
Nonlinear ICA of Temporally Dependent Stationary
Sources
Aapo Hyvärinen and Hiroshi Morioka
Appearing in Proceedings of the 20th International Conference on Artificial Intelligence and
Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA. JMLR: W&CP volume 54.
http://discovery.ucl.ac.uk/1547625/1/AISTATS2017.pdf
Independently Controllable Factors
Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-
Jean Meurs, Joelle Pineau, Doina Precup, Yoshua Bengio
(Submitted on 3 Aug 2017 (v1), last revised 25 Aug 2017 (this version, v2))
Note that there may be several other ways to discover and disentangle underlying factors of
variation. … non-linear versions of ICA (e.g. Hyvärinen and Morioka) attempt to disentangle the
underlying factors of variation by assuming that their joint distribution (marginalizing out the
observed x) factorizes, i.e., that they are marginally independent. Here we explore another
direction, trying to exploit the ability of a learning agent to act in the world in order impose a
further constraint on the representation.

WinstonHide
t-BNE: Tensor-based Brain Network Embedding
Bokai Cao, Lifang He, Xiaokai Wei, Mengqi Xing, Philip S. Yu, Heide Klumpp, Alex D. Leow
http://doi.org/10.1137/1.9781611974973.22
https://www.cs.uic.edu/~bcao1/code/t-BNE.zip
Brain network embedding is the process of converting brain
network data to discriminative representations of subjects,
so that patients with brain disorders and normal controls can
be easily separated. However, existing methods either limit
themselves to extracting graph-theoretical measures and
subgraph patterns, or fail to incorporate brain network
properties and domain knowledge in medical science.
In this paper, we propose t-BNE, a novel Brain Network
Embedding model based on constrained tensor
factorization. t-BNE incorporates:
1) symmetric property of brain networks,
2) side information guidance to obtain representations
consistent with auxiliary measures,
3) orthogonal constraint to make the latent factors distinct
with each other, and
4) classifier learning procedure to introduce supervision
from labeled data
Thebrainnetworkembedding
problemcanbefurther
investigatedinseveraldirections
forfuture work.Forexample,we
wouldliketoworkwith
domainexpertstoincorporate
awidervarietyof guidanceand
supervision(‘medicalknowledge
graph’),andlearna joint
representationfrommultimodal
brainnetworkdata.

WinstonHide
Structural Deep Brain Network Mining
Bokai Cao, Lifang He, Xiaokai Wei, Mengqi Xing, Philip S. Yu, Heide Klumpp, Alex D. Leow
KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
https://doi.org/10.1145/3097983.3097988
Mining from neuroimaging data is becoming
increasingly popular in the field of healthcare and
bioinformatics, due to its potential to discover
clinically meaningful structure patterns that could
facilitate the understanding and diagnosis of
neurological and neuropsychiatric disorders.
In this paper, we propose a Structural Deep Brain
Network mining method, namely SDBN, to learn
highly non-linear and structure-preserving
representations of brain networks. Specifically, we
first introduce a novel graph reordering approach
based on module identification, which rearranges
the order of the nodes to preserve the modular
structure of the graph. (…) Further, it has better
generalization capability for high-dimensional brain
networks and works well even for small sample
learning. Benefit from CNN's task-oriented
learning style, the learned hierarchical
representation is meaningful for the clinical task.
To evaluate the proposed SDBN method, we
conduct extensive experiments on four real brain
network datasets for disease diagnoses. The
experiment results show that SDBN can capture
discriminative and meaningful structural graph
representations for brain disorder diagnosis.
Sincetheproposed
deep featurelearning
frameworkis end-to-
end andtask-
oriented,itsapplication
isnotlimitedtobinary
diseaseclassification.It
can beeasilyextended
totheotherclinical
taskwithobjectives
suchas multi-class
classification,clustering,
regression and
ranking.Weplan toapply
ourframeworkforthe
othermedicaltask.

MikeBarnes
Endotypediscovery and response stratification inImmune-Inflammatorydiseases

MikeBarnes
Endotypediscovery and response stratification inImmune-Inflammatorydiseases #1
Sharedpathology:RheumatoidArthritis(RA),
PsoriasisandSystemicLupusErythematosus
(SLE)
IMIDS–ATreatmentContinuum:
Endotypematch,immunogenicity,disease
evolution,sideeffects(infections,off
“target”)
WhyIMID Endotypesmatter (a)
RandomPatientSelection,(b)Targeted
ClinicalTrial
Jointsofthehand offer anice way toquantify disease
progressionand differentiate pathology types

MikeBarnes
HuntingIMID (Immunomodulatoryimidedrugs)
Endotypes:Response biomarkers,drugendotype,disease
endotypeandMulti-Omics
IMIDBiologictargets–ahighlyconnectedsystem
Endotype identification is important. Same drug can be good for
oneendotype, andbadforanotherendotype

MikeBarnes
TranSMART/i2b2: i2b2 forHealthcare
andHealth InformationSystems;
tranSMART for clinicalresearch.
http://transmartfoundation.org/
https://github.com/transmart
PSORT TranSMART/i2b2:Data
Infrastructrure
LatentClassMixedModels(LCMM)
Findgroupsorsubtypesinmultivariate
categoricaldata.EssentiallyLatentClass
Analysis(LCA)forlongitudinal data
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333702/

MikeBarnes
eMedLab is a hub
http://www.emedlab.ac.uk/
→ IMIDBio-UK
Images, genomic, electronic health records (EHR)
The Francis Crick Institute, UCL, Sanger, Farr, Queen
Mary, EMBL-EBI
The Selfish Scientist “A biologist would rather share their toothbrush
than their (gene) names” - Mike Ashburner, Professor of Genetics, University of Cambridge, UK
from “The Seven Deadly Sins of Bioinformatics” by Carole Goble, The myGrid project, OMII-UK
https://www.slideshare.net/dullhunk/the-seven-deadly-sins-of-bioinformatics
http://dx.doi.org/10.1038/498255a
Data Sharing by Scientists: Practices and Perceptions
Carol Tenopir,Suzie Allard, Kimberly Douglass, Arsev Umur Aydinoglu, Lei Wu, Eleanor Read, Maribeth Manoff, Mike Frame
Published: June 29, 2011
https://doi.org/10.1371/journal.pone.0021101
'Omics Data Sharing
Field et al. (2011) | Science 09 Oct 2009:Vol. 326, Issue 5950, pp. 234-236
http://dx.doi.org/10.1126/science.1180598
Data sharing as social dilemma: Influence of the researcher’s
personality
Linek et al. (2017) | PlOS One
https://doi.org/10.1371/journal.pone.0183216
Scholarly use of social media and altmetrics: A review of the
literature
Sugimoto et al. (2017) | AIS Review
http://doi.org/10.1002/asi.23833
Advantages of a Truly Open-Access Data-Sharing Model
Bertagnolli et al. (2017) | The New England Journal of Medicine
DOI: 10.1056/NEJMsb1702054
A Call for Open-Source Cost-Effectiveness Analysis
Joshua T. Cohen, PhD;Peter J. Neumann, ScD; John B.Wong, MD
Ann Intern Med. 2017 | DOI: 10.7326/M17-1153
Data sharing in clinical trials: An experience with two large cancer
screening trials
Zhu et al.(2017) | PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002304
Scholars in an increasingly open and digital world: imagined
audiences and their impact on scholars’ online participation
Learning,Media and Technology (2017)
http://dx.doi.org/10.1080/17439884.2017.1305966
Principle of proportionality in genomic data sharing
Wright et al. (2016) | Nature Reviews Genetics 17, 1–2 (2016)
http://dx.doi.org/10.1038/nrg.2015.5
OpenfMRI: Open sharing of task fMRI data
Poldrack and Gorgolewski
NeuroImage Volume 144, Part B, January 2017,Pages 259–261

MikeBarnes
Comments and further literature background: DrugSensitivityprediction
Transfer Learning Approaches to Improve Drug
Sensitivity Prediction in Multiple Myeloma Patients
Turki Turki ; Zhi Wei ; Jason T. L. Wang
IEEE Access ( Volume: 5 ) https://doi.org/10.1109/ACCESS.2017.2696523
Compass in the data ocean: Toward chronotherapy
Rikuhiro G. Yamadaa and Hiroki R. Ueda
PNAS May 16, 2017 vol. 114 no. 20
http://dx.doi.org/10.1073/pnas.1705326114
Several reports have shown that internal body time varies by 5–6 h in healthy
humans and by as much as 10–12 h in shift workers. Accumulating evidence
suggests that those misalignments may be a link to health risks, including
obesity (Roenneberg et al. 2012) and psychiatric disorders (Wulff et al. 2010).
Recently, a research group reported that a majority of mammalian genes are
under the clock regulation, and that markedly different genes show circadian
oscillation in each tissue (Zhang et al. 2014). Importantly, they reported that a
substantial number of top-selling drugs in the United States have circadian
targets (Zhang et al. 2014). Based on those findings, a convenient and precise
molecular measurement of tissue molecular time is needed. The report published
in PNAS by Anafi et al. (2017) from the same research group strives to achieve this
precise molecular measurement of tissue molecular time. Petteri: In other
predicting when to administer the best personalized drug
Machine learning identifies a compact gene set for
monitoring the circadian clock in human blood
Jacob J. Hughey
Genome Medicine20179:19
https://doi.org/10.1186/s13073-017-0406-4
Here we used a recently developed method called ZeitZeiger to predict circadian
time (CT, time of day according to the circadian clock) from genome-wide gene
expression in human blood. Our results are an important step towards
precision circadian medicine. In addition, our generalizable extensions to
ZeitZeiger may be applicable to the growing number of biological datasets that
contain multiple observations per individual.

Slava Akmaev
Artificial Intelligence in Biopharma Research and Development

Slava Akmaev
Artificial Intelligence in Biopharma Research and Development
Berg Health Case study: Parkinson’s DiseaseBerg Health Case study: Parkinson’s Disease Berg Health Case study: Parkinson’s Disease (GBA)
9 Computational Drug Discovery
Startups Using AI
APRIL25, 2017 BY NANALYZE
http://www.nanalyze.com/2017/04/9-ai-computational-drug-discovery/

Slava Akmaev
Comments and further literature background
They are now a boring Bayesian company had a lot of problems with traction back in the
day when the big boys did not believe that AI/Machine learning would have any real use
in drug target discovery.
http://doi.org/10.1126/science.1105809 | Cited by 1205 articles
Featuring talk by Marco Scutari, University of Oxford
https://www.slideshare.net/BayesNetsMeetupLondon/bayes-nets-meetup-sept-29th-2016-baye
sian-network-modelling-by-marco-scutari
A network perspective on patient experiences and health
status: the Medical Expenditure Panel Survey 2004 to 2011
Yi-Sheng Chao, Hau-tieng Wu, Marco Scutari, Tai-Shen Chen, Chao-Jung Wu,
Madeleine Durand and Antoine Boivin
BMC Health Services ResearchBMC series – open, inclusive and trusted 2017 17:579
https://doi.org/10.1186/s12913-017-2496-5
https://www.meetup.com/London-Bayesian-network-Meetup-Group/events/233231685/
Granger causality vs. dynamic Bayesian network
inference: a comparative study
Cunlu Zou and Jianfeng Feng BMC Bioinformatics 2009 10:122
https://doi.org/10.1186/1471-2105-10-122
Reverse-engineering biological networks from large
data sets
Joseph L. Natale, David Hofmann, Damian G. Hernández, Ilya Nemenman
(Submitted on 17 May 2017 (v1), last revised 25 May 2017 (this version, v2))

CaswellBarry
What canAIcontributetoNeuroscience?

CaswellBarry
What canAIcontributetoNeuroscience? #1
Problem is not the the computational
power, but to figure out what
information is present, how it is
encoded and what computations are
performed.
And how to generate hypotheses and
models.
Deep learning is inspired by the brain
Perceptron (neuron)
Recurrent networks (e.g. LSTM, hippocampus)
Convolutional networks (cat visual system)
Deep Reinforcement Learning (behaviorism, dopaminergic system)
Deep Neural Networks
Predict response from stimuli
Predict the stimuli from recorded response
Build generative models from these
Let the models build themselves
http://dx.doi.org/10.1038/nature14541

CaswellBarry
Can we decode place cells using RNNs (LSTMs)?
Multielectrode array (MEA) recordings from rodent model
Tampuu A, Barry C, Vicente (in prep.)
Gradient Analysis
Surprisingly (or counterintuitively) the most
informative cells were interneurons firing pretty much
everywhere but with “defined” gradients, while the
least informative cells was rather random (high
entropy).
None of the neuroscience was not actually new and
groundbreaking as admitted by Dr. Barry, but it was
nice to see that the data-driven method came to the
same conclusions as existing literature for example
related to the sides of the place field.
John O’Keefe of University College London
won half of the Nobel prize for his discovery in
1971 of ‘place’ cells in the hippocampus, a part
of the brain associated with memory.
http://dx.doi.org/10.1038/514153a

CaswellBarry
Human Location decoding from fMRI?
Simple spatial memory task in virtual reality environent
DNNs barely exceed SVM performance
Too little data for the used model capacity to actually
overperform SVM. Future exploration for data
augmentation and transfer learning approach (from
“medical imagenet”?)
DNNs make good model of the
visual system possible to decode brain
responses to visual scenes
Deep Neural Networks Reveal a Gradient in the Complexity of Neural
Representations across the Brain's Ventral Visual Pathway Umut Güçlü,
Marcel A. J. van Gerven
(Submitted on 24 Nov 2014)
https://doi.org/10.1523/JNEUROSCI.5023-14.2015
Medical Image Net - Radiology Informatics
http://langlotzlab.stanford.edu/projects/medical-image-net/
https://www.slideshare.net/PetteriTeikariPhD/me
dical-imagenet

CaswellBarry
Future Directions
C. Elegans with its nice 302 neuron system as model
organism for “functional connectome”
Summary
Biological neural networks vs artificial networks (spike trains, no backprop in brain, no
negative firing rates, excitatory and inhibitory neurons)
A Transparent window into biology: A primer
on Caenorhabditis elegans
by AK Corsi - Cited by 80 - Relatedarticles
Non-Associative Learning Representation in the Nervous
System of the Nematode Caenorhabditis elegans
Ramin M. Hasani, Magdalena Fuchs, Victoria Beneder, Radu Grosu
(Submitted on 18 Mar 2017 (v1), last revised 25 Mar 2017 (this version, v3))
SIM-CE: An Advanced Simulink Platform for Studying the
Brain of Caenorhabditis elegans
Ramin M. Hasani, Victoria Beneder, Magdalena Fuchs, David Lung, Radu Grosu
(Submitted on 18 Mar 2017 (v1), last revised 25 Mar 2017 (this version, v3))
https://www.slideshare.net/PetteriTeikari
PhD/prediction-of-art-market
Toward an Integration of Deep
Learning and Neuroscience
HYPOTHESIS & THEORY ARTICLE
Front. Comput. Neurosci., 14 September 2016
http://dx.doi.org/10.3389/fncom.2016.00094
cited by→ Cited by 42 articles

CaswellBarry
Comments and further literature background: Generative models with C.Elegans model
Development of the C. elegans nervous system. (A) C. elegans reaches adulthood
approximately 63 hours after fertilization, over which time its body increases appreciably
in length. (B) In the adult hermaphrodite worm, neurons are distributed unevenly across
the body, with more than 60% being located in the head and about 15% being located in
the tail tip. Here, neurons are color-coded according to their membership to the following
ganglia: anterior [A], dorsal [B], lateral [C], ventral [D], retrovesicular [E], ventral cord [G],
posterior lateral [F], preanal [H], dorsorectal [J], and lumbar [K]. (C) The total number of
neurons (N, solid black), and connections (K, dashed blue), grows nonlinearly but
monotonically with time. (D) A phase transition is evident in the number of synapses as a
function of the number of neurons (yellow circles): before hatching, K grows as N 2 (solid
blue line), whereas after hatching, K grows linearly with N (dashed green line). (Inset) Plot
of the average nodal degree, K, versus number of nodes, N. - Nicosia et al. (2013)
Generative Models for Network Neuroscience:
Prospects and Promise
Richard F. Betzel, Danielle S. Bassett (Submitted on 26 Aug 2017)
Applications using generative models. Model parameters can
be fit to individual subjects and those parameters compared to
some behavioral measures (A) or used to classify different
populations from one another (B). Generative models can also be
used to simulate the development of a biological neural network.
These simulations can be used as forecasting devices to
identify individuals at risk of developing maladaptive network
topologies. They can also be used to explore possible
interventions, e.g. perturbations to parameters or wiring rules,
that drive an individual away from an unfavorable, maladaptive
network topology towards a more favorable state.

CaswellBarry
Commentsandfurther literaturebackground: Backpropagationuseful eveninDeepLearning?
Artificial intelligence pioneer says we need to start over
Steve LeVine Sep 15 2017
https://www.axios.com/ai-pioneer-advocates-starting-over-2485537027.html
Geoffrey Hinton harbors doubts about AI's current workhorse. (Johnny Guatto / University of Toronto)
Hinton, a professor emeritus at the University of Toronto and a Google researcher, said he
is now "deeply suspicious" of back-propagation, the workhorse method that underlies most
of the advances we are seeing in the AI field today, including the capacity to sort through
photos and talk to Siri. "My view is throw it all away and start again," he said.
But Hinton suggested that, to get to where neural networks are able to become intelligent
on their own, what is known as "unsupervised learning," "I suspect that means getting rid
of back-propagation." "I don't think it's how the brain works," he said. "We clearly don't
need all the labeled data."
An Approximation of the Error
Backpropagation Algorithm in a
Predictive Coding Network with
Local Hebbian Synaptic Plasticity
James C. R. Whittington and Rafal Bogacz
http://dx.doi.org/10.1162/NECO_a_00949
Towards Biologically Plausible
Deep Learning
Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein,
Thomas Mesnard, Zhouhan Lin (2016)
The graphical brain: belief
propagation and active inference
Karl J Friston , Thomas Parr and Bert de Vries (2017)
http://dx.doi.org/10.1162/NETN_a_00018
Visual pathways from the
perspective of cost functions and
multi-task deep neural networks
H. Steven Scholte, Max M. Losch, Kandan Ramakrishnan,
Edward H.F. de Haan, Sander M. Bohte (2017)
Neuroscience-Inspired Artificial
Intelligence
Demis Hassabis, Dharshan Kumaran, Christopher
Summerfield, MatthewBotvinick (2017)
Neuron Volume 95, Issue 2, 19 July 2017, Pages 245-258
https://doi.org/10.1016/j.neuron.2017.06.011
Bidirectional Backpropagation:
Towards Biologically Plausible
Error Signal Transmission in
Neural Networks
Hongyin Luo, Jie Fu, James Glass (2017)
"Can the brain do back-propagation?"
Geoffrey Hinton of Google & University of Toronto
https://youtu.be/VIRCybGgHts
Seppo Linnainmaa, (1970). The
representation of the cumulative rounding
error of an algorithm as a Taylor expansion
of the local rounding errors. Master's Thesis
(in Finnish), Univ. Helsinki, 6-7.
“In 1970, Linnainmaa introduced the reverse mode of
automatic differentiation (AD), to efficiently compute the
derivative of a differentiable composite function that can
be represented as a graph, by recursively applying the
chain rule to the building blocks of the function. This
method is now heavily used in numerous applications. For
example, Backpropagation of errors in
multi-layer perceptrons, a technique used in
machine learning, is a special case of reverse mode AD.”

CaswellBarry
Commentsand further literature background: Deep learning & Neuroscience #1A
:
By Petteri Teikari
https://www.slideshare.net/PetteriTeikariPhD/prediction-of-art-market
Neural Encoding and Decoding with Deep
Learning for Dynamic Natural Vision
Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Zhongming Liu
(Submitted on 11 Aug 2016)
Sharing deep generative representation for
perceived image reconstruction from human
brain activity
Changde Du, Changying Du, Huiguang He
(Submitted on 25 Apr 2017 (v1), last revised 11 Jul 2017 (this version, v3))
A primer on encoding models in sensory
neuroscience
Marcel A.J. van Gerven
Journal of Mathematical Psychology Volume 76, Part B, February 2017, Pages 172-183
https://doi.org/10.1016/j.jmp.2016.06.009
Seeing it all: Convolutional network layers map
the function of the human visual system
Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, BertrandThirion
NeuroImage Volume 152, 15 May 2017, Pages 184-194

CaswellBarry
Commentsand further literature background: Deep learning & Neuroscience #1B
:
Generative Models for Network Neuroscience:
Prospects and Promise
Richard F. Betzel, Danielle S. Bassett (Submitted on 26 Aug 2017)
While illuminating, the process of
describing networks in terms of
their topological properties
amounts to an exercise in “fact
collecting.” Though summary
statistics might be useful for
comparing individuals and as
biomarkers of disease, they offer
limited insight into the
mechanisms by which a network
functions, grows, and evolves.
Arguably, one of the overarching
goals of neuroscience (and
biology, in general) is to
manipulate or perturb networks in
targeted and deliberate ways that
result in repeatable and
predictable outcomes. For
network neuroscience to take
steps in addressing this goal, it
must shift its current emphasis
beyond network taxonomy –
i.e. studying subtle individual- or
population-level differences in
summary statistics – towards a
science of mechanisms and
process [22, 23].
Space of generative models. Generative models can be
differentiated from one another along many dimensions,
one of which is the timescale over which they operate. A
model’s timescale is related to its neurobiological
plausibility. Models whose timescale is nearer that of
developmental time can incorporate more realistic and
interpretable features and, in turn, have the chance of
uncovering realistic growth mechanisms (e.g. the model
of C. elegans). At the opposite end of the spectrum are
“single shot” models, e.g. stochastic blockmodels, in
which connection probabilities are initialized early on and
all connections and weights are generated in a single
algorithmic step. Situated between these extremes are
growth models that exhibit intrinsic timescales over which
connections and/or nodes are added to the network, but
where the timescale has no clear biological interpretation
The requisite ingredients An open and
important question that scientists face
when embarking on a study to develop a
generative model is: “What features are
required to build good network
models?” Perhaps the simplest feature
one requires is a target network
topology, the organization of the
network that one is trying to recapitulate
and ultimately explain. Yet, a single
network topology can be built in many
different ways, with strikingly different
underlying mechanisms [52]. Thus one
might also wish to have a deep
understanding of (i) the contraints on
anatomy, from physical distance [53] to
energy consumption [54], (ii) the rules of
neurobiological growth, from
chemical gradients [55] to genetic
specification [56], and (iii) the pressures
of normal or abnormal development, and
their relevance for functionality.
Moreover, each of these constraints,
rules, and pressures can change as the
system grows, highlighting the
importance of developmental timing
[56]. Of course, one might also wish to
choose which of these details to include
in the model, with model parsimony being
one of the key arguments in support of
building models with fewer details.
FUTURE DIRECTIONS More novel
possibility is to use the model for
disease simulation. Many psychiatric
[150] and neurodegenerative diseases
[151] are manifest at the network level in
the form of miswired or dysconnected
systems, but it is unclear what
predisposes an individual to evolve into a
disease state. Similarly, the generative
model can be used to explore in silico the
effect of potential intervention
strategies. We can think of biological
neural networks as living in a
highdimensional space based on their
topological characteristics.
A major hindrance in realizing these
goals, however, is the absence of data
tailored for generative models. The ideal
data would (i) be longitudinal, enabling
one to track and incorporate individual-
level changes over time in the model, and
(ii) include multiple data modalities,
such as functional and structural
connectivity, and genetics, along with
other select factors that could influence
network level organization

CaswellBarry
Commentsand further literature background: Deep learning & Neuroscience #1C
:
Deep adversarial neural decoding
Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier
Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
(Submitted on 19 May 2017 (v1), last revised 15 Jun 2017 (this version, v3))
Here, we present a new approach by combining probabilistic
inference with deep learning, which we refer to as deep
adversarial neural decoding (DAND). Our approach first
inverts the linear transformation from latent features to
observed responses with maximum a posteriori estimation.
Next, it inverts the nonlinear transformation from perceived
stimuli to latent features with adversarial training and
convolutional neural networks. An illustration of our model is
provided in Figure 1. We show that our approach achieves
state-of-the-art reconstructions of perceived faces from the
human brain.
We tested our approach by reconstructing face stimuli
from BOLD responses at an unprecedented level of
accuracy and detail, matching the target stimuli in several
key aspects such as gender, skin color and facial features
as well as identifying perceptual factors contributing to the
reconstruction accuracy. Deep decoding approaches
such as the one developed here are expected to play an
important role in the development of new
neuroprosthetic devices that operate by reading
subjective information from the human brain.
Deep neural networks have been used for classifying or identifying stimuli
via the use of a deep encoding model [Güçlü and M. van Gerven 2015, 2017] or by
predicting intermediate stimulus features [Horikawa and Kamitani 2017,
2017b]. Deep belief networks and convolutional neural networks have been
used to reconstruct basic stimuli (handwritten characters and geometric
figures) from patterns of brain activity [van Gerven et al. 2010, Du et al. 2017].
To date, going beyond such mostly retinotopy-driven reconstructions and
reconstructing complex naturalistic stimuli with high accuracy have proven to
be difficult.

CaswellBarry
Commentsand further literature background: Deep learning & Neuroscience #1D
:
Deep learning with convolutional neural networks for EEG decoding and
visualization
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball
Translational Neurotechnology Lab, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany | BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, Germany
Human Brain Mapping (2017)
http://dx.doi.org/10.1002/hbm.23730
Here we present two
novel methods for
feature visualization
that we used to gain
insights into our ConvNet
learned from the neuronal
data. Here we present
two novel methods for
feature visualization that
we used to gain insights
into our ConvNet learned
from the neuronal
data.The motivation for
developing our
visualization methods
was threefold:
● Verify that the
ConvNets are using
actual brain
signals
● Gain insights into
the ConvNet
behavior, e.g., what
EEG features the
ConvNet uses to
decode the signal
● Potentially make
steps toward using
ConvNets for
brain mapping.
The EEG signal has characteristics that make it different
from inputs that ConvNets have been most successful on,
namely images. In contrast to two-dimensional static images,
the EEG signal is a dynamic time series from electrode
measurements obtained on the three-dimensional scalp
surface. Also, the EEG signal has a comparatively low signal-
to-noise ratio, that is, sources that have no task-relevant
information often affect the EEG signal more strongly than the
task-relevant sources. These properties could make learning
features in an end-to-end fashion fundamentally more difficult
for EEG signals than for images. Thus, the existing ConvNets
architectures from the field of computer vision need to be
adapted for EEG input and the resulting decoding accuracies
rigorously evaluated against more traditional feature extraction
methods. For that purpose, a well-defined baseline is
crucial, that is, a comparison against an implementation of a
standard EEG decoding method validated on published results
for that method. In light of this, in this study, we addressed two
key questions:
● What is the impact of ConvNet design choices (e.g., the
overall network architecture or other design choices such
as the type of nonlinearity used) on the decoding
accuracies?
● What is the impact of ConvNet training strategies (e.g.,
training on entire trials or crops within trials) on the
decoding accuracies?
To address these questions, we created three ConvNets with
different architectures, with the number of convolutional layers
ranging from 2 layers in a “shallow” ConvNet over a 5-layer
deep ConvNet up to a 31-layer residual network (ResNet). All
architectures were adapted to the specific requirements
imposed by the analysis of multi-channel EEG data
Computation overview for input-perturbation network-prediction correlation map.
Absolute input-perturbation network-prediction correlation frequency profile for the deep ConvNet.
Input-perturbation network-
prediction correlation maps
for the deep ConvNet.
Correlation of class
predictions and amplitude
changes. Averaged over all
subjects of the High-Gamma
Dataset.

CaswellBarry
Commentsand further literature background: Deep learning & Neuroscience #2:SpikingandBinary/Terniary Networks
SuperSpike: Supervised learning in
multi-layer spiking neural networks
Friedemann Zenke, Surya Ganguli
Department of Applied Physics, Stanford University
(Submitted on 31 May 2017)
https://arxiv.org/abs/1705.11146 - cited by
BinaryConnect: Training Deep Neural
Networks with binary weights during
propagations
Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Advances in Neural Information Processing Systems 28 (NIPS 2015)
http://papers.nips.cc/paper/5647-binaryconnect-training-deep-neural-networks-with-b
inary-weights-during-propagations
https://github.com/MatthieuCourbariaux/BinaryConnect
Event-Driven Random Back-Propagation:
Enabling Neuromorphic Deep Learning
Machines
Emre O. Neftci, Charles Augustine, Somnath Paul and
Georgios Detorakis | Front Neurosci. 2017; 11: 324.
doi: 10.3389/fnins.2017.00324
Ternary Weight Networks
Fengfu Li, Bo Zhang, Bin Liu
(Submitted on 16 May 2016 (v1), last revised 19 Nov 2016 (this version, v2))
Ternary Residual Networks
Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi, Dheevatsa Mudigere,
Dipankar Das, Bharat Kaul, Pradeep Dubey (Submitted on 15 Jul 2017)
Parallel Computing Lab, Intel Labs
Temporally Efficient Deep Learning
with Spikes
Peter O'Connor, Efstratios Gavves, Max Welling
(Submitted on 13 Jun 2017)
“Intriguingly, this simple communication rule give rise
to units that resemble biologically-inspired leaky
integrate-and-fire neurons, and to a weight-update rule
that is equivalent to a form of Spike-Timing Dependent
Plasticity (STDP), a synaptic learning rule observed in
the brain.”

CaswellBarry
Comments: Braindecodingandmapping in practice: Non-invasive brain “reading”
As if Facebook wasn’t already pervasive enough in
everyday life, the company’s newly formed Building 8
“moon shot” factory is working on adevice they say would
let people type out words via a brain–computer interface
(BCI). Marc Chevillet and his want to build a modified
version of the functional near-infrared
spectroscopy (fNIRS) systems used today for
neuroimaging. Whereas conventional fNIRS systems
work by bouncing light off a tissue sample and analyze all
of the returning photons no matter how diffuse, Building
8’s prosthetic would detect only those photons that have
scattered a small number of times—so-called quasi-
ballistic photons—in order to provide the necessary
spatial resolution.
https://www.scientificamerican.com/article/facebook-launches-
moon-shot-effort-to-decode-speech-direct-from-the-brain/ ELON MUSK WANTS to merge the computer with the
human brain, build a "neurallace," create a "
direct cortical interface," (company called Neuralink).
Bryan Johnson, a Silicon Valley entrepreneur who
previously sold a startup to PayPal for $800 million, is
now building a company called Kernel. He says the
company aims to build a new breed of "neural tools" in
hardware and software—ultimately, in a techno-
utopian way, allowing the brain to do things it has never
done before. In other words, Musk and Johnson are
applying the Silicon Valley playbook to neuroscience.
They're talking about a technology they want to build
well before theycanactuallybuild it.
Researchers could also develop genetic techniques
to modify neurons so that machines can "read and
write" to them from outside our bodies. Or they could
develop nano-robots that we ingest into our bodies
for the same purpose. All this, David Eagleman says, is
moreplausible than animplanted neural lace.
If you strip away all the grandiose language around
these efforts from Johnson and Musk, however,
Eagleman admires what they are doing, mainly
because they are pumping money into research.
"Because they are wealthy, they can set their sights on
a big problem we're trying to solve, and they can work
their waytoward their problem,"he says.
https://www.wired.com/2017/03/elon-musks-neural-lace-really
-look-like/
g.BCIsys - g.tec's Brain-Computer Interface research environment
Complete BCI research system for EEG and EcoG
http://www.gtec.at/Products/Complete-Solutions/g.BCIsys-Specs-Features
Brainmonitoringtakes aleap outofthelab
First-of-its-kinddry EEGsystemcan beusedforreal-lifeapplications
http://ucsdnews.ucsd.edu/pressrelease/brain_monitoring_takes_a_leap_out_of_the_lab
Bioengineersandcognitivescientistshavedeveloped the
firstportable, 64-channelwearablebrain activitymonitoring
system that’scomparabletostate-of-the-artequipment
foundin research laboratories
Mullen et al. (2015): Real-Time Neuroimagingand Cognitive
MonitoringUsing Wearable DryEEG.
NIRx
With NIRSport, you
can measure fNIRS
from anywhere on the
head, in any
environment,
concurrently with
(nearly) any other
modality.
http://nirx.net/nirsport/

CaswellBarry
Comments: Braindecodingandmapping in practice: Non-invasive brain “writing”
Focused ultrasonic neuromodulation
William ‘Jamie’ Tyler lab https://www.tylerlab.com/ultrasonic-neuromodulation/
https://www.theguardian.com/science/2016/nov/07/us-milita
ry-successfully-tests-electrical-brain-stimulation-to-enh
ance-staff-skills
Writing in the journal
Frontiers in Human
Neuroscience, they say
that the technology,
known as transcranial
direct current
stimulation (tDCS, or
tACS), has a “profound
effect”.
Medical device developed by Nexstim achieves very
promising results for stroke patient rehabilitation.
With Nexstim's devices and their use of three-dimensional
structural images of the brain, it is possible to focus the
stimulation accurately (via transcranial magnetic
stimulation, TMS), on the order of millimetres, and thanks
to the EEG recording, we immediately receive information
about changes in the brain's electrical activity,’ says
Professor Risto Ilmoniemi
http://ani.aalto.fi/en/current/news/2014-10-13-003/
http://asci.aalto.fi/en/science_factories/factory_report-coupl
ing_to_the_dynamics_of_the_human_brain_with_tms-eeg/
Ilmoniemi, Risto J., Juha Virtanen, Jarmo Ruohonen,
Jari Karhu, Hannu J. Aronen, and Toivo Katila.
"Neuronal responses to magnetic stimulation
reveal cortical reactivity and connectivity."
Neuroreport 8, no. 16 (1997): 3537-3540.
https://www.ncbi.nlm.nih.gov/pubmed/9427322
Cited by 570 Articles
→ Methodology for combined TMS and EEG
https://www.technologyreview.com/s/542176/a-shocking-way-to-fix-the-brain/
deep brain stimulation (DBS)

SuchiSaria
Can Machines SpotDiseases Fasterthan ExpertHumans?

SuchiSaria
Can Machines SpotDiseases Fasterthan ExpertHumans?
TREWS intelligent pre-emptive system for sepsis detection
at John Hopkins university
Challenges with different sampling rate (e.g. infrequent
creatinine levels vs. continuous heart rate / HRV monitoring)
Personalized medicine for predicting the individualized
response (or identification of phenotypes at more granular
level beyond diagnosis codes).
Petteri: With big data, and data mining, what will happen to
diagnosis codes. Is a person with diabetes+glaucoma e.g.
just the sum of them or something novel with different
response to treatment?

SuchiSaria
Comments and further literature background
Suchi does due diligence for
medical startups very few
startups take into account the
usability of their product, partly due
to lack of access to healthcare
services.
E.g. no clinical practitioner wants to
carry yet another gizmo or start
using yet another software on top
oftheir EpicEHRcrap.
UX and Service Design for Healthcare
https://www.slideshare.net/PetteriTeikariPhD/ux-and-service-design-for-healthcare

SuchiSaria
Comments and further literature background: Irregularandmissingsamples#1
Feature engineering remains a major bottleneck when creating predictive systems from electronic
medical records. At present, an important missing element is detecting predictive regular clinical
motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-
end deep learning system that learns to extract features from medical records and predicts future
risk automatically. Deepr transforms a record into a sequence of discrete elements separated by
coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that
detects and combines predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate Deepr on hospital data to
predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to
traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of
the disease and intervention space. - http://arxiv.org/abs/1607.07519
TREATING MISSING DATA Various options
1. Zero-Imputation Set to zero when missing data
2. FORWARD-FILLING use previous values
3. MISSINGNESS Treat the missing value as a signal, as lack of a value measured e.g. in an ICU can
carry information itself (Lipton et al. 2016)
4. BAYESIAN STATE-SPACE MODELING to fill the missing data (Luttinen et al. 2016, BayesPy package)
5. GENERATIVE MODELING Train the deep network to generate missing samples (Im et al. 2016, RNN
GAN; see also github:sequence_gan)

SuchiSaria
Comments and further literature background: Irregularandmissingsamples#2
http://arxiv.org/abs/1511.02554
Po-Hsiang Chiu, George Hripcsak
Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA
https://doi.org/10.1016/j.jbi.2017.04.009
Learning statistical models of phenotypes using noisy labeled training data
Vibhu Agarwal Tanya Podchiyska Juan M Banda Veena Goel Tiffany I LeungEvan P Minty Timothy E Sweeney Elsie Gyang Nigam H Shah
J Am Med Inform Assoc (2016) 23 (6): 1166-1173.
DOI: https://doi.org/10.1093/jamia/ocw028
A Deep Learning And Novelty Detection Framework For Rapid
Phenotyping In High-Content Screening
C Sommer, R Hoefler, M Samwer, DW Gerlich - bioRxiv, 2017
https://doi.org/10.1101/134627
“Supervised machine learning is a powerful and widely used method to analyze high-
content screening data. Despite its accuracy, efficiency, and versatility, supervised
machine learning has drawbacks, most notably its dependence on a priori knowledge of
expected phenotypes and time-consuming classifier training. We provide a solution to
these limitations with CellCognition Explorer, a generic novelty detection and deep
learning framework. Application to several large-scale image data sets demonstrates that
CellCognition Explorer enables discovery of rare phenotypes without user training, thus
facilitating assay development for high-content screening.”
Data analysis workflows with CellCognition Explorer.
Self-learning of cell object features with CellCognition Deep Learning Module. (a) Schematic illustration
of deep learning using an autoencoder with convolutional, pooling, and fully connected layers. (b) Phenotype
scoring of 2,428 siRNAs (see Fig. 1a) by novelty detection and deep learning using CellCognition Explorer. Red
bars indicate the distribution of the top-100-ranked siRNA hits identified by conventional supervised learning
as in (Held et al., 2010). (c) Comparison of the top-100 screening hits determined either by novelty detection
and deep learning of object features (blue) or supervised learning and conventional features (yellow) for 2,428
siRNAs as in (a, b). Scale bars, 10 m.μ

SuchiSaria
Comments and further literature background: Reinforcementlearningforhealthcare
Continuous State-Space Models for Optimal Sepsis Treatment - a
Deep Reinforcement Learning Approach
Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi
Computer Science and Artificial Intelligence Lab, MIT Cambridge, MA
(Submitted on 23 May 2017) https://arxiv.org/abs/1705.08422
In this work, we propose a new approach
to deduce optimal treatment policies
for septic patients by using continuous
state-space models and deep
reinforcement learning [Deep-Q
Learning (Mnih et al., 2015)]. Learning
treatment policies over continuous spaces
is important, because we retain more of
the patient's physiological information.
Our model is able to learn clinically
interpretable treatment policies,
similar in important aspects to the
treatment policies of physicians.
Evaluating our algorithm on past ICU
patient data, we find that our model could
reduce patient mortality in the
hospital by up to 3.6% over observed
clinical policies, from a baseline mortality
of 13.7%. The learned treatment policies
could be used to aid intensive care
clinicians in medical decision making and
improve the likelihood of patient survival.
We prefer RL for
sepsis treatment over
supervised learning,
because the ground
truth of “good”
treatment strategy
is unclear in medical
literature (Marik,
2015). . Importantly,
RL algorithms also
allow us to infer
optimal strategies
from training
examples that do not
represent optimal
behavior. RL is well-
suited to identifying
ideal septic treatment
strategies, because
clinicians deal with
a sparse, time-
delayed reward
signal in septic
patients, and optimal
treatment strategies
may differ.
AdditionalMedicalReinforcement Learning
literature
Shortreed et al. Informingsequentialclinicaldecision-making
throughreinforcementlearning:an empiricalstudy. Machine
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JohnFox
Datascience meets knowledge engineering:arguments for ahybrid approach

JohnFox
Datascience meets knowledge engineering:arguments for ahybrid approach #1
Francis Timothy (1937-1995)
Pioneer of data science in the NHS
From low-level (e.g. deep learning for detecting lion in
the image) to higher-level (so I see a lion, how should I
react) semantic understanding of data
Knowledge-Data-Knowledge lifecycle. In other words feeding the
“actionable insights” back to the existing knowledge, improving the future
“actionable insights” rather than just creating huge “shallow data lakes” and
go for the “deep data”
Repertoire of http://www.openclinical.org/. If for example Moorfields
creates a good glacuoma care pathway,a hospital in USA or in Zimbabwe
could implement to their context with less “from scratch” work.
Statistical analysis of data in the http://www.openclinical.org/ not very
strong yet, but future incorporation of more intelligent systems for the
knowledge graph is possible in the infrastructure.

JohnFox
Datascience meets knowledge engineering:arguments for ahybrid approach #2
Summary
Knowledge engineering
Knowledge representation
Data science
Analytics (and machine learning)
Hybrid statistical and symbolic learning
Finding nodes in a graph
Peaks and trends in multivariable
distributions suggest existence of
nodes in the knowledge graph

JohnFox
Commentsandfurther literaturebackground: Knowledgegraphandhigh-levelinferenceneeded
Learning a Health Knowledge Graph from
Electronic Medical Records
Maya Rotmensch, YoniHalpern, Abdulhakim Tlimat, Steven Horng & David Sontag
Scientific Reports 7, Article number: 5994 (2017) doi: 10.1038/s41598-017-05778-z
Historically, the models used by diagnostic reasoning systems were
specified manually, requiring tremendous amounts of expert time and
effort. For example, it was estimated that about fifteen person-years
were spent building the Internist-1/QMR knowledge base for internal
medicine. However, the manual specification made these models
extremely brittle and difficult to adapt to new diseases or clinical
settings.
Automatic compilation of a graph relating diseases to the symptoms
that they cause has the potential to significantly speed up the
development of such diagnosis tools. Moreover, such graphs would
provide value in and of themselves. For example, given that general-
purpose web-search engines are among the most commonly consulted
sources for medical information [White and Horvitz 2009;
Hider et al. 2009], health panels such as those provided by Google
using their health knowledge graph have a tremendous potential for
impact [Ramaswami 2015].
EMR data is difficult to interpret for four main reasons: First, the text
of physician and nursing notes is less formal than that of traditional
textbooks, making it difficult to consistently identify disease and
symptom mentions. Second, textbooks and journals often present
simplified cases that relay only the most typical symptoms, to promote
learning. EMR data presents real patients with all of the comorbidities,
confounding factors, and nuances that make them individuals. Third,
unlike textbooks that state the relationships between diseases and
symptoms in a declarative manner, the associations between diseases
and symptoms in the EMR are statistical, making it easy to confuse
correlation with causation. Finally, the manner in which observations
are recorded in the EMR is filtered through the decision-making
process of the treating physician. Information deemed irrelevant may
be omitted or not pursued, leading to information missing not at
random [Weiskopf et al. 2013].
Concept extraction pipeline. Non-negated
concepts and ICD-9 diagnosis codes are
extracted from Emergency Department
electronic medical records. Concepts, codes
and concept aliases are mapped to unique
IDs, which in turn populate a co-occurrence
matrix of size (Concepts) × (Patients).
Workflow of modeling the relationship between
diseases and symptoms and knowledge graph
construction, for each of our 3 models (naive
Bayes, logistic regression and noisy OR).

Edward Perello
DESKGENAIfor CRISPR GenomeEditing #1

Notes on "Artificial Intelligence in Bioscience Symposium 2017"

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