Wikidata and the Semantic Web of Food

ContentMine: Mining the Scientific Literature

Data Citation: A Critical Role for Publishers

Brian Hole

Automatic Extraction of Knowledge from Biomedical literature

Open software and knowledge for MIOSS

Nicole Nogoy's talk at eResearchNZ 2014: Improving data sharing, integration ...

Automatic Extraction of Knowledge from the Literature

Our access to scientific information has changed in ways that were hardly imagined even by the early pioneers of the internet. The immense quantities of data and the array of tools available to search and analyze online content continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of the solutions that it helps to enable. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.

The expansive reach of ChemSpider as a resource for the chemistry community

US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure

Omdi2021 Ontologies for (Materials) Science in the Digital Age

Amanuens.is HUmans and machines annotating scholarly literature

Content Mining of Science in Cambridge

A presentation to Cambridge MPhil Computational Biology. 2020-11-11 . Presenters Peter Murray-Rust, Shweata Hegde and Ambreen Hamadani from https://github.com/petermr/openvirus . This chunk is PMR with a large break in the middle for SH and AH talks. I cover Global Challenges, knowledge equity, semantics of scientific articles, Wikidata, Data Extraction from images, and ethics/politics. Answer: Yes, technically. No, politically as the Publisher-Academic Complex will block it.

Can machines understand the scientific literature?

Alejandra Gonzalez-Beltran

The DATS model: datasets descriptions for data discovery in DataMed

High throughput mining of the scholarly literature; talk at NIH

Content Mining of Science in Europe

Open software and knowledge for MIOSS

Automatic Extraction of Knowledge from Biomedical literature

Cochrane workshop 2016

Was ist angesagt? (20)

Validata: A tool for testing profile conformance

Better Data for a Better World

ContentMine + EPMC: Finding Zika!

ContentMine: Mining the Scientific Literature

Data Citation: A Critical Role for Publishers

Automatic Extraction of Knowledge from Biomedical literature

Open software and knowledge for MIOSS

Nicole Nogoy's talk at eResearchNZ 2014: Improving data sharing, integration ...

Automatic Extraction of Knowledge from the Literature

The expansive reach of ChemSpider as a resource for the chemistry community

Omdi2021 Ontologies for (Materials) Science in the Digital Age

Amanuens.is HUmans and machines annotating scholarly literature

Content Mining of Science in Cambridge

Can machines understand the scientific literature?

The DATS model: datasets descriptions for data discovery in DataMed

High throughput mining of the scholarly literature; talk at NIH

Content Mining of Science in Europe

Open software and knowledge for MIOSS

Automatic Extraction of Knowledge from Biomedical literature

Cochrane workshop 2016

Andere mochten auch

Querying the Wikidata Knowledge Graph

Ioan Toma

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Gene Wiki and Mark2Cure update for BD2K

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Denny Vrandecic

2to3

zubin71

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Alex Faynin

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zubin71

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2016 mem good

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csadhy

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schelby

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EISHI CO. main eps machine catalogue

eishimachinery

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Cominvent AS

Computing on the shoulders of giants

Foredrag på GoOpen, Oslo, 2011 (Norwegian language) NHST Media Group lager nettsidene for bl.a. Dagens Næringsliv, Dagens IT og en rekke engelskspråklige bransjeaviser. Systemutvikler Hans Jørgen Hoel og søke-arkitekt Jan Høydahl forteller om prosessen etter at det ble besluttet å erstatte søkeløsningen fra FAST med fri programvare Apache Solr. Vi vil forsøke å besvare bl.a.: Hvilke utfordringer møtte vi som følge av forskjeller i de to plattformene? Hvorfor bygde vi vårt eget søkerammeverk? Har det nye søket innfridd forventningene? Se også www.goopen.no, www.cominvent.com og www.nhst.no og Twitter hashtag #GoOpen

Dagens Næringslivs overgang til Lucene/Solr søk

Cominvent AS

Channeling Collaborative Spirit

How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks. More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project? A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.

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Querying the Wikidata Knowledge Graph

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2to3

Welcome to Ukraine - SunCity Travel LLC

Fedora Iptables

Scripps bioinformatics seminar_day_2

2016 mem good

B2B Branding Explained

(Bio)Hackathons

2015 6 bd2k_biobranch_knowbio

Resume 2009 Compatible V2 1

2016 bd2k bgood_wikidata

EISHI CO. main eps machine catalogue

Open source breakfast norge findwise

Computing on the shoulders of giants

Dagens Næringslivs overgang til Lucene/Solr søk

Channeling Collaborative Spirit

Ähnlich wie Wikidata and the Semantic Web of Food

Introduction to Biodiversity Informatics

David Shorthouse

Shorthouse

David Shorthouse

AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...

Dr. Haxel Consult

Cornell 2011 05-13

Johannes Keizer

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AIMS (Agricultural Information Management Standards)

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Dario Taraborelli

Scott Edmunds: Data Dissemination in the era of "Big-Data"

Scott Edmunds: GigaScience - Big-Data, Data Citation and Future Data Handling

Scott Edmunds: Revolutionizing Data Dissemination: GigaScience

After centuries with little change, scientific libraries have recently experienced massive upheaval. From being almost entirely paper-based, most libraries are now almost completely digital. This information revolution has all happened in less than 20 years and has created many novel opportunities and threats for scientists, publishers and libraries. Today, we are struggling with an embarassing wealth of digital knowledge on the Web. Most scientists access this knowledge through some kind of digital library, however these places can be cold, impersonal, isolated, and inaccessible places. Many libraries are still clinging to obsolete models of identity, attribution, contribution, citation and publication. Based on a review published in PLoS Computational Biology, http://pubmed.gov/18974831 this talk will discuss the current chilly state of digital libraries for biologists, chemists and informaticians, including PubMed and Google Scholar. We highlight problems and solutions to the coupling and decoupling of publication data and metadata, with a tool called http://www.citeulike.org. This software tool exploits the Web to make digital libraries “warmer”: more personal, sociable, integrated, and accessible places. Finally issues that will help or hinder the continued warming of libraries in the future, particularly the accurate identity of authors and their publications, are briefly introduced. These are discussed in the context of the BBSRC funded REFINE project, at the National Centre for Text Mining (NaCTeM.ac.uk), which is linking biochemical pathway data with evidence for pathways from the PubMed database.

20140623 swets agosti_final

agosti

Transmission6 - Publishing Linked Data

Bill Roberts

TDWG at the University of Tasmania

leebel

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Duncan Hull

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Digital Identity is fundamental to collaboration in bioinformatics research and development because it enables attribution, contribution, publication to be recorded and quantified. However, current models of identity are often obsolete and have problems capturing both small contributions "microattribution" and large contributions "mega-attribution" in Science. Without adequate identity mechanisms, the incentive for collaboration can be reduced, and the utility of collaborative social tools hindered. Using examples of metabolic pathway analysis with the taverna workbench and myexperiment.org, this talk will illustrate problems and solutions to identifying scientists accurately and effectively in collaborative bioinformatics networks on the Web.

Wikis at work

Dan Bolser

myExperiment @ Nettab

Duncan Hull

Global Information Systems for Plant Genetic Resources (2009)

Dag Endresen

Scott Edmunds: Data Dissemination in the era of "Big-Data"

dkNET Poster ENDO 2016

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Ähnlich wie Wikidata and the Semantic Web of Food (20)

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Shorthouse

AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...

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Ciard Initiative and a Global Infrastructure for Linked Open Data

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Scott Edmunds: Data Dissemination in the era of "Big-Data"

Scott Edmunds: GigaScience - Big-Data, Data Citation and Future Data Handling

Scott Edmunds: Revolutionizing Data Dissemination: GigaScience

20140623 swets agosti_final

Transmission6 - Publishing Linked Data

TDWG at the University of Tasmania

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Scott Edmunds: GigaScience - a journal or a database? Lessons learned from th...

Wikis at work

myExperiment @ Nettab

Global Information Systems for Plant Genetic Resources (2009)

Scott Edmunds: Data Dissemination in the era of "Big-Data"

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Mehr von Benjamin Good

Representing and reasoning with biological knowledge

The Gene Ontology (GO) Consortium (GOC) is developing a new knowledge representation approach called ‘causal activity models’ (GO-CAM). A GO-CAM describes how one or several gene products contribute to the execution of a biological process. In these models (implemented as OWL instance graphs anchored in Open Biological Ontology (OBO) classes and relations), gene products are linked to molecular activities via semantic relationships like ‘enables’, molecular activities are linked to each other via causal relationships such as ‘positively regulates’, and sets of molecular activities are defined as ‘parts’ of larger biological processes. This approach provides the GOC with a more complete and extensible structure for capturing knowledge of gene function. It also allows for the representation of knowledge typically seen in pathway databases. Here, we present details and results of a rule-based transformation of pathways represented using the BioPAX exchange format into GO-CAMs. We have automatically converted all Reactome pathways into GO-CAMs and are currently working on the conversion of additional resources available through Pathway Commons. By converting pathways into GO-CAMs, we can leverage OWL description logic reasoning over OBO ontologies to infer new biological relationships and detect logical inconsistencies. Further, the conversion helps to increase standardization for the representation of biological entities and processes. The products of this work can be used to improve source databases, for example by inferring new GO annotations for pathways and reactions and can help with the formation of meta-knowledge bases that integrate content from multiple sources.

Integrating Pathway Databases with Gene Ontology Causal Activity Models

Pathways2GO: Converting BioPax pathways to GO-CAMs

Knowledge Beacons

When the Heart BD2K grant was originally written. We proposed to build something called “Big Data World” to help advance citizen science, scientific crowdsourcing and science education – especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways.

Science Game Lab

PubMed now indexes roughly 25 million articles and is growing by more than a million per year. The scale of this “Big Knowledge” repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge. Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses. Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed. It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts. While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center’s ‘Implicitome’ technology. Users of Knowledge.Bio begin by identifying a concept of interest using text search. Once a concept is identified, associated triples and concept-pairs are displayed in tables. These tables have text-based and semantic filters to help refine the list of triples to relations of interest. The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js. The graph is used as a note-taking or ‘mind-mapping’ structure that can be saved offline and then later reloaded into the application. Clicking on edges within a graph or on the ‘evidence’ element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles. Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the “Big Knowledge” scattered across the biomedical literature. Application: http://knowledge.bio Source code: https://bitbucket.org/sulab/kb1/

(Poster) Knowledge.Bio: an Interactive Tool for Literature-based Discovery

Citizen sciencepanel2015 pdf

The life sciences are faced with a rapidly growing array of technologies for measuring the molecular states of living things. From sequencing platforms that can assemble the complete genome sequence of a complex organism involving billions of nucleotides in a few days to imaging systems that can just as rapidly churn out millions of snapshots of cells, biology is truly faced with a data deluge. To translate this information into new knowledge that can guide the search for new medicines, biomedical researchers increasingly need to build on the existing knowledge of the broad community. Prior knowledge can help guide searches through the masses of new data. Unfortunately, most biomedical knowledge is represented solely in the text of journal articles. Given that more than a million such articles are published every year, the challenge of using this knowledge effectively is substantial. Ideally, knowledge such as the interrelations between genes, drugs and diseases would be represented in a knowledge graph that enabled queries like: “show me all the genes related to this disease or related to any drugs used to treat this disease”. Systems exist that attempt to extract this information automatically from text, but the quality of their output remains far below what can be obtained by human readers. We are developing a new platform that taps the language comprehension abilities of citizen scientists to help excavate a queryable knowledge graph from the biomedical literature. In proof-of-concept experiments, we have demonstrated that lay-people are capable of extracting meaningful information from complex biological text. The information extracted using this community intelligence framework can surpass the efforts of individual experts in quality while also offering the potential to achieve massive scale. In this presentation we will describe the results of early experiments and introduce our prototype citizen science platform: http://mark2cure.org.

Building a massive biomedical knowledge graph with citizen science

A crucial task in modern biology is the prediction of complex phenotypes, such as breast cancer prognosis, from genome-wide measurements. Machine learning algorithms can sometimes infer predictive patterns, but there is rarely enough data to train and test them effectively and the patterns that they identify are often expressed in forms (e.g. support vector machines, neural networks, random forests composed of 10s of thousands of trees) that are highly difficult to understand. In addition, it is generally unclear how to include prior knowledge in the course of their construction. Decision trees provide an intuitive visual form that can capture complex interactions between multiple variables. Effective methods exist for inferring decision trees automatically but it has been shown that these techniques can be improved upon via the manual interventions of experts. Here, we introduce Branch, a new Web-based tool for the interactive construction of decision trees from genomic datasets. Branch offers the ability to: (1) upload and share datasets intended for classification tasks (in progress), (2) construct decision trees by manually selecting features such as genes for a gene expression dataset, (3) collaboratively edit decision trees, (4) create feature functions that aggregate content from multiple independent features into single decision nodes (e.g. pathways) and (5) evaluate decision tree classifiers in terms of precision and recall. The tool is optimized for genomic use cases through the inclusion of gene and pathway-based search functions. Branch enables expert biologists to easily engage directly with high-throughput datasets without the need for a team of bioinformaticians. The tree building process allows researchers to rapidly test hypotheses about interactions between biological variables and phenotypes in ways that would otherwise require extensive computational sophistication. In so doing, this tool can both inform biological research and help to produce more accurate, more meaningful classifiers. A prototype of Branch is available at http://biobranch.org/

Branch: An interactive, web-based tool for building decision tree classifiers

Serious games for bioinformatics education. ISMB 2014 education workshop

Background: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility and biological interpretability. Methods that take advantage of structured prior knowledge (e.g. protein interaction networks) show promise in helping to define better signatures but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes previously unheard of. Objective: The main objective of this study was to test the hypothesis that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from players of an open, Web-based game. We envisioned capturing knowledge both from the player’s prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. Methods: We developed and evaluated an online game called “The Cure” that captured information from players regarding genes for use in predictors of breast cancer survival. Information gathered from game play was aggregated using a voting approach and used to create rankings of genes. The top genes from these rankings were evaluated using annotation enrichment analysis, comparison to prior predictor gene sets, and by using them to train and test machine learning systems for predicting 10-year survival. Results: Between its launch in Sept. 2012 and Sept. 2013, The Cure attracted more than 1,000 registered players who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data showed significant enrichment for genes known to be related to key concepts such as Cancer, Disease Progression, and Recurrence (P < 1.1e-07). In terms of the accuracy of models trained using them, these gene sets provided comparable performance to gene sets generated using other methods including those used in commercial tests. The Cure is available at http://genegames.org/cure/

The Cure: Making a game of gene selection for breast cancer survival prediction

Benjamin M. Good, Max Nanis, Andrew I. Su Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses that would otherwise be impossible. As a result, many biological natural language processing (BioNLP) projects attempt to address this challenge. However, the state of the art in BioNLP still leaves much room for improvement in terms of precision, recall and the complexity of knowledge structures that can be extracted automatically. Expert curators are vital to the process of knowledge extraction but are always in short supply. Recent studies have shown that workers on microtasking platforms such as Amazon’s Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text. Here, we investigated the use of the AMT in capturing disease mentions in Pubmed abstracts. We used the recently published NCBI Disease corpus as a gold standard for refining and benchmarking the crowdsourcing protocol. After merging the responses from 5 AMT workers per abstract with a simple voting scheme, we were able to achieve a maximum f measure of 0.815 (precision 0.823, recall 0.807) over 593 abstracts as compared to the NCBI annotations on the same abstracts. Comparisons were based on exact matches to annotation spans. The results can also be tuned to optimize for precision (max = 0.98 when recall = 0.23) or recall (max = 0.89 when precision = 0.45). It took 7 days and cost $192.90 to complete all 593 abstracts considered here (at $.06/abstract with 50 additional abstracts used for spam detection). This experiment demonstrated that microtask-based crowdsourcing can be applied to the disease mention recognition problem in the text of biomedical research articles. The f-measure of 0.815 indicates that there is room for improvement in the crowdsourcing protocol but that, overall, AMT workers are clearly capable of performing this annotation task.

Poster: Microtask crowdsourcing for disease mention annotation in PubMed abst...

Microtask crowdsourcing for disease mention annotation in PubMed abstracts Benjamin M. Good, Max Nanis, Andrew I. Su Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses that would otherwise be impossible. As a result, many biological natural language processing (BioNLP) projects attempt to address this challenge. However, the state of the art in BioNLP still leaves much room for improvement in terms of precision, recall and the complexity of knowledge structures that can be extracted automatically. Expert curators are vital to the process of knowledge extraction but are always in short supply. Recent studies have shown that workers on microtasking platforms such as Amazon’s Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text. Here, we investigated the use of the AMT in capturing disease mentions in Pubmed abstracts. We used the recently published NCBI Disease corpus as a gold standard for refining and benchmarking the crowdsourcing protocol. After merging the responses from 5 AMT workers per abstract with a simple voting scheme, we were able to achieve a maximum f measure of 0.815 (precision 0.823, recall 0.807) over 593 abstracts as compared to the NCBI annotations on the same abstracts. Comparisons were based on exact matches to annotation spans. The results can also be tuned to optimize for precision (max = 0.98 when recall = 0.23) or recall (max = 0.89 when precision = 0.45). It took 7 days and cost $192.90 to complete all 593 abstracts considered here (at $.06/abstract with 50 additional abstracts used for spam detection). This experiment demonstrated that microtask-based crowdsourcing can be applied to the disease mention recognition problem in the text of biomedical research articles. The f-measure of 0.815 indicates that there is room for improvement in the crowdsourcing protocol but that, overall, AMT workers are clearly capable of performing this annotation task.

Microtask crowdsourcing for disease mention annotation in PubMed abstracts

Mark2Cure: a crowdsourcing platform for biomedical literature annotation

The Cure: A Game with the Purpose of Gene Selection for Breast Cancer Surviva...

Gene Wiki at Phenotype RCN annual meeting

Short update on The Cure game first week

An online game for human phenotype prediction