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This is a short presentation I gave at the Pistoia Alliance Meeting on our Mobile Strategy at RSC and what we have at present and where our vision will take us.
Chemistry apps in the wild and at the royal society of chemistry
Chemistry apps in the wild and at the royal society of chemistry
US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
GAIN COMPETITIVE EDGE WITH TRG’S SEMANTIC TECHNOLOGY
Faster R & D Analysis Tool - TRG
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TRG
Over 15 years ago, Sir Tim Berners Lee proclaimed the founding of an exciting new future involving intelligent agents operating over smarter data in order to perform complex tasks at the behest of their human controllers. At the heart of this vision lies an uneasy alliance between tedious formal knowledge representations and powerful analytics over big, but often messy data. Bio2RDF, our decade old open source project to create Linked Data for the life sciences, has weaved emergent Semantic Web technologies such as ontologies and Linked Data to generate FAIR - Findable, Accessible, Interoperable, and Reusable - data in the form of billions of machine accessible statements for use in downstream biomedical discovery. This revolution in data publication has been strengthened by action from global bioinformatics institutions such as the NCBI, NCBO, EBI, and DBCLS. Notably, NCBI's PubChem has successfully coupled large scale data integration with community-based standards to offer a remakable biochemical knowledge resource amenable to data hungry discovery tools. Yet, in the face of increasing pressure from researchers, funders, and publishers, will these approaches be sufficient for growing and maintaining a comprehensive knowledge graph that is inclusive of all biomedical research?
Building a Network of Interoperable and Independently Produced Linked and Ope...
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Nonadaptive mastermind algorithms for string and vector databases, with case ...
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Slides from the Pistoia Alliance European Conference in London on Tuesday 19th April 2016.
Pistoia Alliance conference April 2016: Big Data: Mathew Woodwark
Pistoia Alliance conference April 2016: Big Data: Mathew Woodwark
Pistoia Alliance
Introduction to block chain technology and its applications in healthcare.
Application of blockchain technology in healthcare and biomedicine
Application of blockchain technology in healthcare and biomedicine
Pranavathiyani G
Presented at D4 2020 The presentation focuses on the application of knowledge graph approach in profiling drug targets. The speaker, Nolan Nichols, highlights the potential of genetic modifiers in developing transformative therapies for patients suffering from diseases such as spinal muscular atrophy (SMA). The company, Maze Therapeutics, is utilizing advanced data science and collaboration with AWS healthcare and life sciences to access and analyze meaningful human genetics data, and build a cloud-based data architecture. The presentation also covers the use of semantic technologies in drug discovery, target discovery, and target validation, and the integration of proprietary and shared data through the knowledge graph. The presentation concludes with a summary of the company's launch in 2019 with a $190 million investment and its focus on translating genetic modifying insights into new therapeutics.
Focus on the Evidence: a knowledge graph approach to profiling drug targets
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Nolan Nichols
An overview to machine learning and some recent paper on application of deep learning in biology
Machine learning in biology
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This is a short presentation I gave at the Pistoia Alliance Meeting on our Mobile Strategy at RSC and what we have at present and where our vision will take us.
Chemistry apps in the wild and at the royal society of chemistry
Chemistry apps in the wild and at the royal society of chemistry
US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
GAIN COMPETITIVE EDGE WITH TRG’S SEMANTIC TECHNOLOGY
Faster R & D Analysis Tool - TRG
Faster R & D Analysis Tool - TRG
TRG
Over 15 years ago, Sir Tim Berners Lee proclaimed the founding of an exciting new future involving intelligent agents operating over smarter data in order to perform complex tasks at the behest of their human controllers. At the heart of this vision lies an uneasy alliance between tedious formal knowledge representations and powerful analytics over big, but often messy data. Bio2RDF, our decade old open source project to create Linked Data for the life sciences, has weaved emergent Semantic Web technologies such as ontologies and Linked Data to generate FAIR - Findable, Accessible, Interoperable, and Reusable - data in the form of billions of machine accessible statements for use in downstream biomedical discovery. This revolution in data publication has been strengthened by action from global bioinformatics institutions such as the NCBI, NCBO, EBI, and DBCLS. Notably, NCBI's PubChem has successfully coupled large scale data integration with community-based standards to offer a remakable biochemical knowledge resource amenable to data hungry discovery tools. Yet, in the face of increasing pressure from researchers, funders, and publishers, will these approaches be sufficient for growing and maintaining a comprehensive knowledge graph that is inclusive of all biomedical research?
Building a Network of Interoperable and Independently Produced Linked and Ope...
Building a Network of Interoperable and Independently Produced Linked and Ope...
Michel Dumontier
Nonadaptive mastermind algorithms for string and vector databases, with case ...
Nonadaptive mastermind algorithms for string and vector databases, with case ...
Ecway Technologies
Slides from the Pistoia Alliance European Conference in London on Tuesday 19th April 2016.
Pistoia Alliance conference April 2016: Big Data: Mathew Woodwark
Pistoia Alliance conference April 2016: Big Data: Mathew Woodwark
Pistoia Alliance
Introduction to block chain technology and its applications in healthcare.
Application of blockchain technology in healthcare and biomedicine
Application of blockchain technology in healthcare and biomedicine
Pranavathiyani G
Presented at D4 2020 The presentation focuses on the application of knowledge graph approach in profiling drug targets. The speaker, Nolan Nichols, highlights the potential of genetic modifiers in developing transformative therapies for patients suffering from diseases such as spinal muscular atrophy (SMA). The company, Maze Therapeutics, is utilizing advanced data science and collaboration with AWS healthcare and life sciences to access and analyze meaningful human genetics data, and build a cloud-based data architecture. The presentation also covers the use of semantic technologies in drug discovery, target discovery, and target validation, and the integration of proprietary and shared data through the knowledge graph. The presentation concludes with a summary of the company's launch in 2019 with a $190 million investment and its focus on translating genetic modifying insights into new therapeutics.
Focus on the Evidence: a knowledge graph approach to profiling drug targets
Focus on the Evidence: a knowledge graph approach to profiling drug targets
Nolan Nichols
An overview to machine learning and some recent paper on application of deep learning in biology
Machine learning in biology
Machine learning in biology
Pranavathiyani G
Fuzzy net works using a multidimensional view of data have become very popular in both business and science in recent years. Fuzzy net works for fuzzy purposes such as medicine and bio-chemistry1 pose several great challenges to existing fuzzy net work technology. Fuzzy net works usually use pre-aggregated data to ensure fast query response. However, pre-aggregation cannot be used in practice if the dimension structures or the relationships between facts and dimensions are irregular. A technique for overcoming this limitation and some experimental results are presented. Queries over fuzzy fuzzy net works often need to reference data that is external to the fuzzy net work, e.g., data that is too complex to be handled by current fuzzy net work technology, data that is â€owned†by other organizations, or data that is updated frequently. This paper presents a federation architecture that allows the integration of multidimensional warehouse data with complex external data.
Hybrid Fuzzy Approches for Networks
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ijcoa
Presentation at the eScience Institute on eagle-i and identification of research resources. http://nnlm.gov/2013escience/
eScience Institute presentation on eagle-i
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A presentation to the New Year's Event for Maastricht University's Knowledge Engineering @ Work Program. https://www.maastrichtuniversity.nl/news/kework-first-10-students-academic-workstudy-track-graduate
Data Science for the Win
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Michel Dumontier
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
Link Analysis of Life Sciences Linked Data
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Deep learning: Alexander Ivliev
Pistoia Alliance USA Conference 2016
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Pistoia Alliance
Presentation I gave as part of the selection process for my current position as Data Curation Editor at the Nature journal, Scientific Data.
Why should researchers care about data curation?
Why should researchers care about data curation?
Varsha Khodiyar
Bio2RDF is an open-source project that offers a large and connected knowledge graph of Life Science Linked Data. Each dataset is expressed using its own vocabulary, thereby hindering integration, search, query, and browse data across similar or identical types of data. With growth and content changes in source data, a manual approach to maintain mappings has proven untenable. The aim of this work is to develop a (semi)automated procedure to generate high quality mappings between Bio2RDF and SIO using BioPortal ontologies. Our preliminary results demonstrate that our approach is promising in that it can find new mappings using a transitive closure between ontology mappings. Further development of the methodology coupled with improvements in the ontology will offer a better-integrated view of the Life Science Linked Data
2016 bmdid-mappings
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Current CV for David Tyrpak
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Expert panel on industrialising microbiomics - with Unilever
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MiBio - The new generation CASA system
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Trilobita Informatics Excl. Co.
Biomedical researchers will remain stymied in their ability to take full advantage of the Big Data revolution if they can never find the datasets that they need to analyze, if there is lack of clarity about what particular datasets contain, and if data are insufficiently described. CEDAR, an NIH BD2K Center of Excellence, aims to develop methods and tools to vastly ease the burden of authoring good experimental metadata, and to maximally use this information to zero in on datasets of interest.
Making it Easier, Possibly Even Pleasant, to Author Rich Experimental Metadata
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The poster was made during a 2.5 day NSF sponsored workshop on August 5th – 7th on the University of Washington, Seattle campus, which brought together 100 graduate students from diverse domain sciences and engineering with Data Scientists from industry and academia to discuss and collaborate on Big Data / Data Science challenges. In addition to keynote presentations from high profile speakers, the participants presented posters covering their own research and worked collaboratively to begin to solve some of the Grand Challenge problems facing Data Enabled Science & Engineering disciplines. This presentation was made by 10 graduate students, whose united them was high-dimensional biological data. For more info, see http://depts.washington.edu/dswkshp/
X team 2 - presentation
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WGBMEResume
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A presentation to the DBMI department at the University of Pittsburgh about data sharing and reuse: what this means, why it is important, some of what we’ve learned, and what we still don’t know.
Why study Data Sharing? (+ why share your data)
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Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
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SgCodeJam24 Workshop Extract
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to be updated very soon (today/tomorrow)
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Fuzzy net works using a multidimensional view of data have become very popular in both business and science in recent years. Fuzzy net works for fuzzy purposes such as medicine and bio-chemistry1 pose several great challenges to existing fuzzy net work technology. Fuzzy net works usually use pre-aggregated data to ensure fast query response. However, pre-aggregation cannot be used in practice if the dimension structures or the relationships between facts and dimensions are irregular. A technique for overcoming this limitation and some experimental results are presented. Queries over fuzzy fuzzy net works often need to reference data that is external to the fuzzy net work, e.g., data that is too complex to be handled by current fuzzy net work technology, data that is â€owned†by other organizations, or data that is updated frequently. This paper presents a federation architecture that allows the integration of multidimensional warehouse data with complex external data.
Hybrid Fuzzy Approches for Networks
Hybrid Fuzzy Approches for Networks
ijcoa
Presentation at the eScience Institute on eagle-i and identification of research resources. http://nnlm.gov/2013escience/
eScience Institute presentation on eagle-i
eScience Institute presentation on eagle-i
mhaendel
A presentation to the New Year's Event for Maastricht University's Knowledge Engineering @ Work Program. https://www.maastrichtuniversity.nl/news/kework-first-10-students-academic-workstudy-track-graduate
Data Science for the Win
Data Science for the Win
Michel Dumontier
Semantic web technologies offer a potential mechanism for the representation and integration of thousands of biomedical databases. Many of these databases offer cross-references to other data sources, but these are generally incomplete and prone to error. In this paper, we conduct an empirical analysis of the link structure of life science Linked Data, obtained from the Bio2RDF project. Three different link graphs for datasets, entities and terms are characterized by degree, connectivity, and clustering metrics, and their correlation is measured as well. Furthermore, we utilize the symmetry and transitivity of entity links to build a benchmark and evaluate several popular entity matching approaches. Our findings indicate that the life science data network can help find hidden links, can be used to validate links, and may offer a mechanism to integrate a wider set of resources to support biomedical knowledge discovery.
Link Analysis of Life Sciences Linked Data
Link Analysis of Life Sciences Linked Data
Michel Dumontier
Deep learning: Alexander Ivliev
Pistoia Alliance USA Conference 2016
Pistoia Alliance USA Conference 2016
Pistoia Alliance
Presentation I gave as part of the selection process for my current position as Data Curation Editor at the Nature journal, Scientific Data.
Why should researchers care about data curation?
Why should researchers care about data curation?
Varsha Khodiyar
Bio2RDF is an open-source project that offers a large and connected knowledge graph of Life Science Linked Data. Each dataset is expressed using its own vocabulary, thereby hindering integration, search, query, and browse data across similar or identical types of data. With growth and content changes in source data, a manual approach to maintain mappings has proven untenable. The aim of this work is to develop a (semi)automated procedure to generate high quality mappings between Bio2RDF and SIO using BioPortal ontologies. Our preliminary results demonstrate that our approach is promising in that it can find new mappings using a transitive closure between ontology mappings. Further development of the methodology coupled with improvements in the ontology will offer a better-integrated view of the Life Science Linked Data
2016 bmdid-mappings
2016 bmdid-mappings
Michel Dumontier
Current CV for David Tyrpak
David Tyrpak CV
David Tyrpak CV
DavidTyrpak1
A panel of experts, including Dr Barry Murphy, Microbiomics Science Lead at Unilever, Dr Craig McAnulla, Senior Consultant for Bioinformatics and Dr Yasmin Alam-Faruque, Scientific Data Manager/Biocurator discuss first-hand experience and views on how to get better insights faster from microbiome data.
Expert panel on industrialising microbiomics - with Unilever
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Eagle Genomics
METAGENOTE_poster2016
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Mariam Quiñones
Presenter: Jens Hoefkens, Director of Strategic Marketing, Research Analytics PerkinElmer Informatics, Inc. BioIT World Expo 2017 May 24th, 2017
Democratizing Data Science: Balancing Flexibility and Usability for Scientifi...
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PerkinElmer Informatics
Building a Global Search Engine for Genetic Data for #DevTO 2018.
Building a Global Search Engine for Genetic Data
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Miro Cupak
Startup: Pine
Pistoia Alliance USA Conference 2016
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Species-specific semen analysis, accurate assessments. The MiBio project is a unique experiment in the semen analysis discipline with the aim of consolidating the analysis methodologies and metrics, and additionally, creating a community driven knowledge base.
MiBio - The new generation CASA system
MiBio - The new generation CASA system
Trilobita Informatics Excl. Co.
Biomedical researchers will remain stymied in their ability to take full advantage of the Big Data revolution if they can never find the datasets that they need to analyze, if there is lack of clarity about what particular datasets contain, and if data are insufficiently described. CEDAR, an NIH BD2K Center of Excellence, aims to develop methods and tools to vastly ease the burden of authoring good experimental metadata, and to maximally use this information to zero in on datasets of interest.
Making it Easier, Possibly Even Pleasant, to Author Rich Experimental Metadata
Making it Easier, Possibly Even Pleasant, to Author Rich Experimental Metadata
Michel Dumontier
The poster was made during a 2.5 day NSF sponsored workshop on August 5th – 7th on the University of Washington, Seattle campus, which brought together 100 graduate students from diverse domain sciences and engineering with Data Scientists from industry and academia to discuss and collaborate on Big Data / Data Science challenges. In addition to keynote presentations from high profile speakers, the participants presented posters covering their own research and worked collaboratively to begin to solve some of the Grand Challenge problems facing Data Enabled Science & Engineering disciplines. This presentation was made by 10 graduate students, whose united them was high-dimensional biological data. For more info, see http://depts.washington.edu/dswkshp/
X team 2 - presentation
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WGBMEResume
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A presentation to the DBMI department at the University of Pittsburgh about data sharing and reuse: what this means, why it is important, some of what we’ve learned, and what we still don’t know.
Why study Data Sharing? (+ why share your data)
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Heather Piwowar
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale. The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
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2016 bmdid-mappings
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Democratizing Data Science: Balancing Flexibility and Usability for Scientifi...
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An extract of the full deck of the SgCodeJame24 Workshop about SciVerse and OpenSocial platform and applications
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to be updated very soon (today/tomorrow)
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