Introduction to 16S rRNA gene multivariate analysisJosh Neufeld
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What makes a linked data pattern interesting?Szymon Klarman
A short talk on the problem of mining linked data (RDF) patterns, introducing a few preliminary notions towards the definition of generic linked data mining algorithms.
large data set is not available for some disease such as Brain Tumor. This and part2 presentation shows how to find "Actionable solution from a difficult cancer dataset
Introduction to 16S rRNA gene multivariate analysisJosh Neufeld
Short introductory talk on multivariate statistics for 16S rRNA gene analysis given at the 2nd Soil Metagenomics conference in Braunschweig Germany, December 2013. A previous talk had discussed quality filtering, chimera detection, and clustering algorithms.
What makes a linked data pattern interesting?Szymon Klarman
A short talk on the problem of mining linked data (RDF) patterns, introducing a few preliminary notions towards the definition of generic linked data mining algorithms.
large data set is not available for some disease such as Brain Tumor. This and part2 presentation shows how to find "Actionable solution from a difficult cancer dataset
Visualization Approaches for Biomedical Omics Data: Putting It All TogetherNils Gehlenborg
Keynote Talk presented at the 1st Annual BiVi Community Annual Meeting (17 December 2014)
http://bivi.co/page/bivi-annual-meeting-16-17th-december-2014
Visualization Approaches for Biomedical Omics Data: Putting It All Together
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In my talk I will discuss how visualization approaches can be applied to enable exploration and support analysis of data generated by such studies. Specifically, I will review techniques and tools for visual exploration of individual omics data types, their ability to scale to large numbers of individuals or samples, and emerging techniques that integrate multiple omics data types for interactive visual analysis. I will also examine technical and legal challenges that developers of such visualization tools are facing. To conclude my talk, I will outline research opportunities for the biological data visualization community that address major challenges in this domain.
What comes after science?
Two types of technology: man-made technology and universe-centered technology
Two types of data: invariant data and variant data
How Life Is Lived In Different Times
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• Statisticians contributed a large inferential framework, important Bayesian perspectives, the bootstrap and CART and random forests, and the concepts of sparsity and parsimony.
• Computer scientists contributed an appetite for big, challenging problems.They also pioneered neural networks, boosting, PAC bounds, and developed programming languages, such as Spark and hadoop, for handling Big Data.
• Mathematicians contributed support vector machines, modern optimization, tensor analysis, and (maybe) topological data analysis.
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Traditional computing techniques and systems consider a main process device or main server, and technique details generally
serially. They're non-robust and non-adaptive, and have limited quantity. Indifference, scientific technique details in a very similar and allocated manner, while not a main management. They're exceedingly strong, elastic, and ascendible. This paper offers a short conclusion of however the ideas from biology are will never to style new processing techniques and techniques that even have a number of the beneficial qualities of scientific techniques. Additionally, some illustrations are a device given of however these techniques will be used in details security programs.
Visualization Approaches for Biomedical Omics Data: Putting It All TogetherNils Gehlenborg
Keynote Talk presented at the 1st Annual BiVi Community Annual Meeting (17 December 2014)
http://bivi.co/page/bivi-annual-meeting-16-17th-december-2014
Visualization Approaches for Biomedical Omics Data: Putting It All Together
The rapid proliferation of high quality, low cost genome-wide measurement technologies such as whole-genome and transcriptome sequencing, as well as advances in epigenomics and proteomics, are enabling researchers to perform studies that generate heterogeneous datasets for cohorts of thousands of individuals. A common feature of these studies is that a collection of genome-wide, molecular data types and phenotypic or clinical characterizations are available for each individual. These data can be used to identify the molecular basis of diseases and to characterize and describe the variations that are relevant for improved diagnosis, prognosis and targeted treatment of patients. An example for a study in which this approach has been successfully applied is The Cancer Genome Atlas project (http://cancergenome.nih.gov).
In my talk I will discuss how visualization approaches can be applied to enable exploration and support analysis of data generated by such studies. Specifically, I will review techniques and tools for visual exploration of individual omics data types, their ability to scale to large numbers of individuals or samples, and emerging techniques that integrate multiple omics data types for interactive visual analysis. I will also examine technical and legal challenges that developers of such visualization tools are facing. To conclude my talk, I will outline research opportunities for the biological data visualization community that address major challenges in this domain.
What comes after science?
Two types of technology: man-made technology and universe-centered technology
Two types of data: invariant data and variant data
How Life Is Lived In Different Times
Course slides for computational phyloinformatics, an annual course organized by NESCent in collaboration with hosting organizations across the world. I am the teacher of the Perl section of the course, these are the slides I presented in 2010 at BGI, Shenzhen, PRC.
Tales from BioLand - Engineering Challenges in the World of Life SciencesStefano Di Carlo
Prof. Alfredo Benso from SysBio Group @ Politecnico di Torino keynote presentation at ICIIBMS - IEEE International Conference on Intelligent Informatics and BioMedical Sciences, on Nov 26 2017 in Okinawa (Japan).
Although hierarchical organization is highly prevalent in natural, technological and social systems, we are far from a general quantitative understanding of the essence of hierarchy and its generative mechanisms. A first step towards such a theory is to be able to measure the level of hierarchical organization of a system. Based on the framework of directed networks, I will discuss previous attempts to construct such a measure relying on different intuitive ideas, and then I present a possible axiomatic framework to classify these measures and to understand their relation to each other. Finally, a new, computationally efficient measure is introduced based on random walks on the network, being the first one satisfying all of the axioms.
Data science is an area at the interface of statistics, computer science, and mathematics.
• Statisticians contributed a large inferential framework, important Bayesian perspectives, the bootstrap and CART and random forests, and the concepts of sparsity and parsimony.
• Computer scientists contributed an appetite for big, challenging problems.They also pioneered neural networks, boosting, PAC bounds, and developed programming languages, such as Spark and hadoop, for handling Big Data.
• Mathematicians contributed support vector machines, modern optimization, tensor analysis, and (maybe) topological data analysis.
Measuring Social Complexity and the Emergence of Cooperation from Entropic Pr...IJEAB
The quantitative assessment of the state and dynamics of a social system is a very difficult problem. This issue is important for both practical and theoretical reasons such as establishing the efficiency of social action programs, detecting possible community needs or allocating resources. In this paper we propose a new general theoretical framework for the study of social complexity, based on the relation of complexity and entropy in combination with evolutionary dynamics to assess the dynamics of the system. Imposing the second law of thermodynamics, we study the conditions under which cooperation emerges and demonstrate that it depends on the relative importance of local and global fitness. As cooperation is a central concept in sustainability, this thermodynamic-informational approach allows new insights and means to assess it using the concept of Helmholtz free energy. We then introduce a new set of equations that consider the more general case where the social system changes both in time and space, and relate our findings to sustainability. Finally we present a model for the collapse of Rapa Nui island civilization in NetLogo. We applied our approach to measure both the entropy production and the complexity of the system and the results support our purpose that sustainability needs a positive entropy production regime which is related to cooperation emergence.
Survey: Biological Inspired Computing in the Network SecurityEswar Publications
Traditional computing techniques and systems consider a main process device or main server, and technique details generally
serially. They're non-robust and non-adaptive, and have limited quantity. Indifference, scientific technique details in a very similar and allocated manner, while not a main management. They're exceedingly strong, elastic, and ascendible. This paper offers a short conclusion of however the ideas from biology are will never to style new processing techniques and techniques that even have a number of the beneficial qualities of scientific techniques. Additionally, some illustrations are a device given of however these techniques will be used in details security programs.
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Updated (version 2.3 THRILLER) Easy Perspective to (Complexity)-Thriller 12 Slides.pdf
1. 1
Probability, Causality, Intricacy, and Emergence
“Complexity Space”
An Easy & Structured Approach to the CONCEPTS of :
(Complexity Theory), (Probability & Disorder),
(Causality and Feedback) and (Complex Systems)
Complexity Theory
HABIB’s Complexity 3D Perspective
Harmonizing the Concepts of Probability, Causality, Intricacy, and Emergence.
Downloadable (for free) for Non-members
(and is : Virus, Malignancy, and Macro Free)
VERSION 2.3 , September 5th 2022
To get the Latest Version: Open https://www.slideshare.net/EmadfHABIB2/
You will Find ONLY ONE File Named :
“UPDATED (Version <whatever>) Easy (Complexity Theory) … “ ,
While other files are named “Outdated” or have a Completely Different Name (Other Presentations)
Eng. Emad Farag HABIB
2. 2
( Quotes )
“Complexity science is so important in today's world ..
Many of the most important problems
in Engineering, Medicine, and Public Policy
are now addressed with the ideas and methods of complexity science.”
James Ladyman (University of Bristol), Karoline Wiesner (Universität Potsdam), August 2020 ,
DOI:10.12987/yale/9780300251104.001.0001
And Author’s book “What is a complex system?” (published with Yale University Press)
“Complexity is A MULTI-FACETED Phenomenon,
involving a variety of features .. “
( same a/m authors )
“A variety of Different Measures would be required
to capture all our intuitive ideas
about what is meant by complexity”
The late Physics Nobel Laureate : “MurrayGell-Mann”
Complexity Theory
HABIB’s Complexity 3D Perspective
Harmonizing the Concepts of Probability, Causality, Intricacy, and Emergence.
ad Farag HABIB
4-Realms :
Probability
Causality
Intricacy
Emergence
Importance of Complexity:
Complexity “Space” :
3. 3
( Quotes )
“ ... to begin thinking along the LINES of complexity theory.
Future Scholars and Scholar-Practitioners
will need to think and act Differently
when facing Complexity. “
John R. Turner and Rose M. Baker :
Complexity Theory An Overview with Potential Applications for the Social Sciences ; doi:10.3390/systems7010004
“Focusing on Information Flow
will help us to understand better
how cells and organisms work.”
Nobel Laureate Paul Nurse
Complexity Theory
HABIB’s Complexity 3D Perspective
Harmonizing the Concepts of Probability, Causality, Intricacy, and Emergence.
ad Farag HABIB
4-Realms :
Probability
Causality
Intricacy
Emergence
Complexity Importance & “Space” :
Complexity Core-Issue is “Information Flow” :
5. 5
HABIB’s Complexity 3D Perspective
Eng Emad Farag HABIB
Entropy Meaning
Main Ref.: A. LESNE, “Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics”
Understanding the Meaning of Entropy (in Different Sciences): (ref: "Crossroads", Annick Lesne 2011 )
Topic High Entropy String (H=0.9, H=15,..) Low Entropy String (H=0.1,H=0.3,..)
Basics:
Information High Information = Less repeated pattern Low Information = More repeated pattern
Predictability Low Predictability = High Uncertainity High Predictability = Low Uncertainity
Typicality of Disorder Low "Typicality" (High Rarity) Disorder High "Typicality" (Low Rarity) Disorder
Unevenness High Unevenness = Symmetry-breaking Low Unevenness = High-Symmetry
Ex: words like: "aztdn", "odrcr" (from "Wenglish") words like: "ABCDEFGH", ~ "qu….."
IT:
#Digits Large #Digits required to store the info Few #Digits required to store the info
Shannon Entropy (Math.) less Correlated String: Entropy "H" (H~=log2(N) ) Correlated String: "h" (h << log2(N), Dep.)
Indep. Of String-symbols more Independent Sequence more Dep. Seq.[Symbols'-Distr/ Time-Correl.]
Redundancy Scarcely Redundant (Highly distinct) Highly Redundant (scarcely distinct)
%Compressibility Scarcely Compressible (Highly informative) Highly Compressible (scarcely informative)
Missing Info (average) = average I. required to specify the outcome x when the receiver knows the distribution p = amount of uncertainty represented by a pro
Large Missing Information = Large P.Distr. Uncertainity Little Missing Information = Low P.Distr. Uncertainity
Algorithmic Length Large (long) Algorithm to regenerate a String Small (short) Algorithm to regenerate a String
#Ways to (compose) string Few #Ways Many #Ways
Context Uncommon string (within current context) Common string (within current context)
Ex: # : 3.1623 , 3.1103755(another context: √10, π in Octal) # : 4444444, 2468
Probability: [ 1: ELH // 2: P.Distr. : Random Var X, p(x) // 3: Sequences: X, p(x), Types, SubTypes! ]
Uniformity (Elements-wise) more Equal-likelihood Elements less Equal-likelihood Elements
Uniformity (Classes-wise) Similar Classes DisSimilar Classes
Distribution: Event-described ! Distr. Is composed (fully) of Common Events Distr. Is composed (fully) of Rare Events
#States (Possible): TODO Expectation, @states, H,,
Large #: 3(added)dice=4.17 > 1 die=2.58> coin=1 Small #States: coin tossing ( log2|x|=1)
Ex: P. Distr "in/of" string: #Digits to Describe the string
"Normal" (inside 6σ set of values/events) "Normal" (outside 6σ set of values/events)
Dynamical Systems:
#Categories,Elements Large #Categories & Sparse #Elements Few #Categories & Dense #Elements
Ex: Bio. Molecules Protein Structures, Immune-System Cell-Types Simple Structures
VIMP: in Immune System: Healthy: Entropy "booms" @∆ T-Cells & B-Cells ! Eldery ?: minor ∆H: even @large ∆ of Immune threats
Stat. Physics:
( Concerning: Entropy Production "by/via" a dissipative system, rather than Entropy "in/of" the system : Thermodynamic "S" rather than Statistical "H" )
Microstate Molecules: Gas M. are ALL at the same state Molecules: Gas M. are at Different states
Macrostate System: Unable to do useful (mechanical) Work System: Able to do useful (mechanical) Work
Gases Gas in One thermodynamic Compartment Gas in Two thermodynamic Compartments
Ex: P. Distr "by/via" system: S= #Digits of Emergence ! (to Estimate possible Useful work, as opposed to "pure Dissipation")
6. 6
HABIB’s Complexity 3D Perspective
Eng Emad Farag HABIB
Appendix E: Links & Interrelations in Systems
Dichotomous Classification of Feedback
Enough research done in this regard ?
Information
Correlated Uncorrelated
Dependent Independent
Flow of Information No Flow
Directed Flow Non-directed flow
Predictive
(Extrapolative)
Non-predictive
(Non-extrapolative)
Transfer (TE) Non-Transfer
Causal Non-causal
Circular Causality Direct Causality
-FDBK
Servo
(Follows a Variable setpoint)
Regulation
( Follows a Fixed Setpoint)
+FDBK
7. 7
HABIB’s Complexity 3D Perspective
Eng Emad Farag HABIB
Complexity Space
(A Coherent Perspective)
Viewing Complexity as a 3D Information Space
(# 2 of 4: Complexity Measures : Types & Examples)
Axis X Y Z
Axis-Title Orderness Causality (Feedback) Intricacy
System Part
("Scope")
Environ / Sys Sys / Subsys Subsys / Subsys
Complexity
Measures
How to Describe the
system
How to Build the system System's Degree of Organization
(Elements-wise).
Measures
Examples
Information/ Entropy/
Algorithmic Complexity/
Min. Description
Length/ Renyi/ Fractal
(macro) Dimension
Logical Depth/
Thermodynamic D./
Computational
Complexity (,Time,
Space)/ Information-
Based C.
Fractal D. (micro!)/ Sophistication/
Effective Measure C./ Hierarchical
C./ Tree Subgraph/
Homogeneous.
8. 8
HABIB’s Complexity 3D Perspective
Eng Emad Farag HABIB
A Coherent Perspective to Complexity
Axis X Y (Y-Z Shared !) Z Info Aspects
Axis-Title Orderness Causality (Feedback) Intricacy
System Part
("Scope")
Environ / Sys Sys / Subsys (Inter-Subsys) Subsys / Subsys Info Domains
Main Phenomena Macro Properties, Pattern
formation.
Feedback
(Coded Symbolic).
(Building &
Organizing)
( the SubStr)
Self-Organization
(Subsys, Elements).
Info Usage,
Outcomes
Examples Thermodynamics(PV=
nRT),Fractals, Swarms,
Flocks
Comm: Sampling Rates (2X), mRNA,
Regulatory (Signaling) Pathways?
(Physiology)
(mRNA Vaccines
Marvel! )
Immune Antibodies Diversification
(Germinal Centers)
Info Norms
Quantification Entropy measure: (T.D.,
Shannon)
Hard!, Indirect via: [Non-
Linearity & (Info-)Agents
Formation]
Transfer Entropy ,
…
Measures of: Sophistication,
Hierarchical C., Tree subgraph.
Info
Measures
Main Feature Notion of ~Gestalt Notion of ~Classes Notion of ~Typicality Notion of ~Elements I. Concern
Complexity
Measures
How to Describe the system How to Build the system ( Str / Shared Info) System's Degree of Organization
(Elements-wise).
MIT paper: Info
Measures
Measures
Examples
Information/ Entropy/
Algorithmic Complexity/
Min. Description Length/
Renyi/ Fractal (macro)
Dimension
Logical Depth/ Thermodynamic
D./ Computational Complexity
(,Time, Space)/ Information-
Based C.
(Algorithmic Mutual Info/
Channel Capacity/
Correlation/ Stored Info/
Transfer/ Organization )
Fractal D. (micro!)/ Sophistication/
Effective Measure C./ Hierarchical C./ Tree
Subgraph/ Homogeneous.
MIT Paper by
"Seth Lloyd"
[#3: Str. /
Shared Mutual
Info. ]
~Scale ~macro ~meso (meso-micro) ~micro Info ~Scale
Follows, Guided
by, ..
Simple Rules!
( Statistical)
Communication Rules ( [Speciality/
Numerosity] )
Balance/Duality: [Specifity/
Diversification]
Info "Envelops"
Limits? Spatio-Temporal Limits:
Saturation, Clipping,..
Communication Limits,
Smartness of Agents
( N.A. ! : already
between 2 Extremes)
None!! : Pure Random ! // then
select/elect by -ve Feedback ?
Info
Asymptotes
Info "Types"
(semiotics)
Syntactic (~Form, Objects) Semantic (~Correlations,
relations)
( Learning ) Pragmatic (~Subjective,
Beholder, User)
I. Qualitative
Aspects
Entropy
Concentration
theorems
Sequence space
(Alphabet)
Classes of Sequences (=Types) (Max. Entropy
Distribution? )
Elements (Symbols) I. (Entropy)
Concentration
Comm. Ex. : a "data string" (aggr.) its interpretation its measurement an example
(Action By), the
"Computer"
Sys (not Environ) De-centralized !! (SubSys) De-centralized : just the
(Elements), No "Organizer" !!
Info
Computation
~ ~ Western
Science-Schools
German Science-School:
Constructivism ?
British Science-School:
Empricism ?
American Science-School:
Pragmatism ?
Knowledge
Approach ?
Notes Pattern formation: can be
Scale-free!
VIMP: +veFDBK LIMITS!: e.g. :
Resources, Saturation, Traffic, ..
(Shared Features : can be
considered Y or Z),
~"Transition Features"
Traditional (Classical) Science:
ceases at a Complexity of 3 Elements
!!
Eng. Emad Farag
Habib, Nov 2021
Abbrev.: Information/ System/ Diversification/ Aggregate/ ThermoDynamics/ Feedback/ Complexity (C.) / Communication (Comm.)/ Example/ Not Applicable/ Very Important/ Dimension
9. 9
HABIB’s Complexity 3D Perspective
Eng Emad Farag HABIB
4-Realms :
Probability
Causality
Intricacy
Emergence
DISORDER
+Feedback
(Causality)
+Intricacy
Complex Adaptive Systems
(CAS)
Emergence & Adaptability
( Spontaneous or Equilibrium-based)
==LIMIT ==
+(more ?!)
+1
+FDBK:
Too much -FDBK:
-2 r/n
- r/n
-1,-2, ..
Slight -FDBK
Direct Causality
(non-causal)
ORDER
S0
S1
S0 S1 S2
[ Initial Conditions “External Disturbance” System Adaptation ]
The System responds by “Adapting” to : [ more Intricacy More Info flow More Order ]
Shift from S0 to S1 : Starts Externally (Order), then midway (Causality), then Internally (Intricacy)
Shift from S1 to S2 : Starts Internally (Intricacy), then midway (Causality), then Externally (Order)
S2
CAS System Response
[ More: Order, Links, Intricacy]
10. 10
Set of facts:
( regarding Complexity Theory and ultra-Conservative beliefs )
( Complexity theory is Not the theory of everyhting )
1- Not Contradicting with beliefs: just Intersecting
2- Intersecting in few issues: that have already settled long ago :
Evolution, Randomity, Eventual Thermal Death of the Universe, .. etc.
3- It adds nothing to either sides of the debates or controversies ! :
i.e.: those wishing to see Complexity as pro-Evolution: will percieve it so,
and those wishing to see Complexity as against-Evolution: will pervieve it so .
4- For a “Non-Scientific Believer” : If such theories causes doubts,
they should be skipped and simply left for specialists’.
However: for a “Scientific Believer”: Complexity Theory
can be of a good & constructive value ..
5- Complexity Theory is NOT pseudoscience. Complexity Theory shares with other
sciences the Benefits that ALL Sciences have:
that knowledge is good ! , and despite human beings have limited knowledge,
Such Knowledge can be developed more, by Studying & Researching,
to discover more laws & facts pointing at a Wise-Creator ..
Complexity Theory
HABIB’s Complexity 3D Perspective
Harmonizing the Concepts of Probability, Causality, Intricacy, and Emergence.
What Complexity
Theory is NOT
11. 11
.. and What Complexity
Theory is
Ref:
John R. Turner and Rose M. Baker :
“Complexity Theory: An Overview with
Potential Applications for the Social
Sciences”
University of North Texas, 2019
doi:10.3390/systems7010004
Complexity Theory
HABIB’s Complexity 3D Perspective
Harmonizing the Concepts of Probability, Causality, Intricacy, and Emergence.
12. 12
Ver 0.1 20211207 (but was deleted from internet !!!)
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Ver 0.4 20211212
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Ver 0.9 20211224
Ver 1.0 20220101
Ver 1.01 20220103 (Online January 2nd 2022)
Ver 1.2 20220104
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VERSION 1.6 20220123
VERSION 1.7 20220124(Online February 10th 2022)
VERSION 1.8 20220211(Online February 11th 2022)
VERSION 1.9 20220216
Ver 2.0 20220222
VERSION 2.1 20220301
VERSION 2.2 20220312 ( PDF Thriller 12 Slides only: 20220320)
VERSION 2.3 20220905 ( PDF Thriller 12 Slides only: 20220905)
Your CRITICISM is Highly Required
and any REQUEST of the source file (Concepts file “ XLS” )
is also welcomed :
SystemThinking@Inbox.LV
( You can revisit the “Conclusion” Slide )
This Presentation is a draft,
will be updated and uploaded later.
( Draft Presentation: due to Author’s Suffering & the need for a Scientific Research Funding or Grant
To continue researching such Subject )