2011-10-11 Open PHACTS at BioIT World Europeopen_phacts
The document discusses the Innovative Medicines Initiative's Open PHACTS project, which aims to develop robust standards and apply them in a semantic integration platform ("Open Pharmacological Space") to integrate drug discovery data from various public and private sources. The project brings together partners from industry, academia, and non-profits to build an open infrastructure for linking drug discovery knowledge and supporting ongoing research. It outlines the technical approach, priorities, and initial progress on developing exemplar applications and a prototype "lash up" system.
Presented by Richard Kidd at "The Future Information Needs of Pharmaceutical & Medicinal Chemistry", Monday 28 November 2011 at The Linnean Society, Burlington Square, London run by the RSC CICAG group.
The information revolution has transformed many business sectors over the last decade and the pharmaceutical industry is no exception. Developments in scientific and information technologies have unleashed an avalanche of content on research scientists who are struggling to access and filter this in an efficient manner. Furthermore, this domain has traditionally suffered from a lack of standards in how entities, processes and experimental results are described, leading to difficulties in determining whether results from two different sources can be reliably compared. The need to transform the way the life-science industry uses information has led to new thinking about how companies should work beyond their firewalls. In this talk we will provide an overview of the traditional approaches major pharmaceutical companies have taken to knowledge management and describe the business reasons why pre-competitive, cross-industry and public-private partnerships have gained much traction in recent years. We will consider the scientific challenges concerning the integration of biomedical knowledge, highlighting the complexities in representing everyday scientific objects in computerised form. This leads us to discuss how the semantic web might lead us to a long-overdue solution. The talk will be illustrated by focusing on the EU-Open PHACTS initiative (openphacts.org), established to provide a unique public-private infrastructure for pharmaceutical discovery. The aims of this work will be described and how technologies such as just-in-time identity resolution, nanopublication and interactive visualisations are helping to build a powerful software platform designed to appeal to directly to scientific users across the public and private sectors.
2015-02-10 The Open PHACTS Discovery Platform: Semantic Data Integration for ...open_phacts
The Open PHACTS Discovery Platform integrates multiple biomedical data resources into a single open access point using semantic web technology. It is guided by business questions from pharmaceutical companies to integrate data from sources like ChEMBL, DrugBank, UniProt, and more. The platform is run as a public-private partnership through 2021 to support drug discovery.
Data Integration vs Transparency: Tackling the tensionPaul Groth
Paul Groth discussed the tension between data integration and transparency. He explained that while integrating data from multiple sources is important for analysis, it can reduce transparency about where the data came from. Provenance, or recording the origin and process of data, was presented as a solution. Groth outlined challenges in provenance collection and proposed techniques like taint tracking and record and replay from software security to help automate provenance capture while data is integrated and analyzed.
Big Data and the Health domain (vis-a-vis the respective H2020 Societal Challenge) - Opportunities, Challenges and Requirements. As presented and discussed in the public launch of the BigDataEurope project.
The document discusses online resources that can support open drug discovery systems. It outlines how pharmaceutical companies spend billions annually on R&D and how public domain data from sources like literature, patents and databases could provide high value. However, such data is difficult to integrate and navigate due to a lack of standards and interoperability between sources. The Open PHACTS project aims to address this by developing standards to semantically integrate drug discovery data from public and private sources.
EDF2014: Paul Groth, Department of Computer Science & The Network Institute, ...European Data Forum
Invited Talk by Paul Groth, Department of Computer Science & The Network Institute, VU University Amsterdam, Netherlands at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Open PHACTS: A Data Platform for Drug Discovery.
2011-10-11 Open PHACTS at BioIT World Europeopen_phacts
The document discusses the Innovative Medicines Initiative's Open PHACTS project, which aims to develop robust standards and apply them in a semantic integration platform ("Open Pharmacological Space") to integrate drug discovery data from various public and private sources. The project brings together partners from industry, academia, and non-profits to build an open infrastructure for linking drug discovery knowledge and supporting ongoing research. It outlines the technical approach, priorities, and initial progress on developing exemplar applications and a prototype "lash up" system.
Presented by Richard Kidd at "The Future Information Needs of Pharmaceutical & Medicinal Chemistry", Monday 28 November 2011 at The Linnean Society, Burlington Square, London run by the RSC CICAG group.
The information revolution has transformed many business sectors over the last decade and the pharmaceutical industry is no exception. Developments in scientific and information technologies have unleashed an avalanche of content on research scientists who are struggling to access and filter this in an efficient manner. Furthermore, this domain has traditionally suffered from a lack of standards in how entities, processes and experimental results are described, leading to difficulties in determining whether results from two different sources can be reliably compared. The need to transform the way the life-science industry uses information has led to new thinking about how companies should work beyond their firewalls. In this talk we will provide an overview of the traditional approaches major pharmaceutical companies have taken to knowledge management and describe the business reasons why pre-competitive, cross-industry and public-private partnerships have gained much traction in recent years. We will consider the scientific challenges concerning the integration of biomedical knowledge, highlighting the complexities in representing everyday scientific objects in computerised form. This leads us to discuss how the semantic web might lead us to a long-overdue solution. The talk will be illustrated by focusing on the EU-Open PHACTS initiative (openphacts.org), established to provide a unique public-private infrastructure for pharmaceutical discovery. The aims of this work will be described and how technologies such as just-in-time identity resolution, nanopublication and interactive visualisations are helping to build a powerful software platform designed to appeal to directly to scientific users across the public and private sectors.
2015-02-10 The Open PHACTS Discovery Platform: Semantic Data Integration for ...open_phacts
The Open PHACTS Discovery Platform integrates multiple biomedical data resources into a single open access point using semantic web technology. It is guided by business questions from pharmaceutical companies to integrate data from sources like ChEMBL, DrugBank, UniProt, and more. The platform is run as a public-private partnership through 2021 to support drug discovery.
Data Integration vs Transparency: Tackling the tensionPaul Groth
Paul Groth discussed the tension between data integration and transparency. He explained that while integrating data from multiple sources is important for analysis, it can reduce transparency about where the data came from. Provenance, or recording the origin and process of data, was presented as a solution. Groth outlined challenges in provenance collection and proposed techniques like taint tracking and record and replay from software security to help automate provenance capture while data is integrated and analyzed.
Big Data and the Health domain (vis-a-vis the respective H2020 Societal Challenge) - Opportunities, Challenges and Requirements. As presented and discussed in the public launch of the BigDataEurope project.
The document discusses online resources that can support open drug discovery systems. It outlines how pharmaceutical companies spend billions annually on R&D and how public domain data from sources like literature, patents and databases could provide high value. However, such data is difficult to integrate and navigate due to a lack of standards and interoperability between sources. The Open PHACTS project aims to address this by developing standards to semantically integrate drug discovery data from public and private sources.
EDF2014: Paul Groth, Department of Computer Science & The Network Institute, ...European Data Forum
Invited Talk by Paul Groth, Department of Computer Science & The Network Institute, VU University Amsterdam, Netherlands at the European Data Forum 2014, 20 March 2014 in Athens, Greece: Open PHACTS: A Data Platform for Drug Discovery.
2014-03-20 Open PHACTS - A Data Platform for Drug Discoveryopen_phacts
A data platform is proposed for drug discovery that would lower industry firewalls and enable pre-competitive data integration, analysis, and reuse across pharmaceutical companies. The platform would integrate external research data from literature, databases, and other sources on compounds, targets, pathways, and diseases. It would provide data integration and analysis tools through a firewalled database system and applications. The goal is to advance drug discovery by allowing multiple companies to access and build upon the same large foundation of pre-competitive research data.
This document discusses the challenges of integrating public domain drug discovery data from multiple sources and the mission of the Open PHACTS Foundation to address this issue. It outlines Open PHACTS' approach of integrating biomedical data resources into a single open access point using semantic web technologies and standards. This will allow users to perform queries across multiple data sources and analyze related data in an integrated manner. The document also notes some of the technical challenges around data integration due to differences in formats, identifiers and languages between sources.
Big data supporting drug discovery - cautionary tales from the world of chemi...Valery Tkachenko
The document discusses how big data and machine learning can support drug discovery efforts. It describes the Royal Society of Chemistry's (RSC) ChemSpider database, which contains over 30 million chemicals and is a central resource for searching chemical structures. The document outlines how RSC is working to improve data quality, enable semantic search of literature, and develop tools to help navigate the large chemical space in order to support drug discovery research. RSC is also working on data management, crowdsourcing, and building connections between databases to further advance chemistry research.
Domains such as drug discovery, data science, and policy studies increasing rely on the combination of complex analysis pipelines with integrated data sources to come to conclusions. A key question then arises is what are these conclusions based upon? Thus, there is a tension between integrating data for analysis and understanding where that data comes from (its provenance). In this talk, I describe recent work that is attempting to facilitate transparency by combining provenance tracked within databases with the data integration and analytics pipelines that feed them. I discuss this with respect to use cases from public policy as well as drug discovery.
Given at: http://ccct.uva.nl/content/ccct-seminar-21-february-2014
Slides to be presented at a webinar arranged by Metasolution as part of a Vinnova project http://metasolutions.se/2014/03/webbinarium-med-kerstin-forsberg-om-lankade-data-i-lakemedelsforskningen/
Data-driven drug discovery for rare diseases - Tales from the trenches (CINF ...Frederik van den Broek
Slides from my talk at the ACS CINF Symposium on Collaborations & Data Sharing in Rare & Orphan Disease Drug Discovery on 31 March 2019 in Orlando.
Abstract:
For the pharmaceutical industry as a whole, addressing the challenge of rare or orphan diseases is high on the agenda. But for the patients and their families, rare diseases can be very isolating and it can often feel like the potential for new treatments is low. One avenue for potential treatments is to identify drug repurposing candidates for the rare disease in question. This talk will give an overview of various collaborative projects undertaken in the last few years, which involved the combination, normalisation and analysis of data from various disparate sources, including some valuable lessons learnt along the way.
IUPHAR/BPS Guide to Pharmacology: concise mapping of chemistry, data, and tar...Chris Southan
The document summarizes the IUPHAR/BPS Guide to Pharmacology (GtoPdb) database, which maps relationships between chemistry, data, and protein targets. It has evolved from earlier databases to now include over 1500 human protein targets linked to ligand data. Challenges include resolving relationships across different target hierarchies and filling data gaps. Future plans include expanding the database and linking it to immunopharmacology data through a new Guide to Immunopharmacology portal.
With its focus on investigating the basis for the sustained existence
of living systems, modern biology has always been a fertile, if not
challenging, domain for formal knowledge representation and automated
reasoning. With thousands of databases and hundreds of ontologies now
available, there is a salient opportunity to integrate these for
discovery. In this talk, I will discuss our efforts to build a rich
foundational network of ontology-annotated linked data, develop
methods to intelligently retrieve content of interest, uncover
significant biological associations, and pursue new avenues for drug
discovery. As the portfolio of Semantic Web technologies continue to
mature in terms of functionality, scalability, and an understanding of
how to maximize their value, researchers will be strategically poised
to pursue increasingly sophisticated KR projects aimed at improving
our overall understanding of human health and disease.
bio: Dr. Michel Dumontier is an Associate Professor of Medicine
(Biomedical Informatics) at Stanford University. His research aims to
find new treatments for rare and complex diseases. His research
interest lie in the publication, integration, and discovery of
scientific knowledge. Dr. Dumontier serves as a co-chair for the World
Wide Web Consortium Semantic Web in Health Care and Life Sciences
Interest Group (W3C HCLSIG) and is the Scientific Director for
Bio2RDF, a widely used open-source project to create and provide
linked data for life sciences.
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...Frederik van den Broek
Slides from my talk at the ACS CINF Symposium on Chemical Nomenclature & Representation on 26 August 2019 in San Diego.
Abstract:
The first edition of the Beilstein Handbook of Organic Chemistry was published nearly 140 years ago. Electronic laboratory notebooks have been in use in chemistry for almost 20 years. And the life science industry still doesn't have a well-defined way of capturing and exchanging information about chemical reactions and relies on imprecise or vendor-specific data formats. Without a common language and structure to describe experiments, data integration is unnecessarily expensive and a significant part of published data has not been readily available for processing or analysis.
The Unified Data Model (UDM) project team aims to improve the situation. UDM is a collective effort of vendors and life science organizations to create an open, extendable and freely available reference model and data format for exchange of experimental information about compound synthesis and testing. Run under the umbrella of the Pistoia Alliance, the project team has published two releases of the UDM data format and it is expected that the model will continue to be improved as demand stipulates working with the Pistoia FAIR data implementation by industry community.
Curatorial data wrangling for the Guide to PHARMACOLGY Chris Southan
This document discusses the challenges and experiences of curating quantitative target-ligand interaction data for the Guide to PHARMACOLOGY database from the primary literature. Standardizing entities such as proteins, ligands, and measurement units across different data sources can be difficult due to inconsistencies in naming, identifiers, and reporting of values. Initiatives by publishers to have authors mark up key entities in manuscripts may help curators but will not solve all compatibility issues. The curation process also requires judgment calls on issues like resolving conflicts between data sources and determining whether reported data can be structurally defined.
This document discusses how APIs are enabling new ways of accessing and mashing up drug discovery data from various sources. It provides examples of existing bio/chem/med APIs and case studies of companies that are using APIs in novel ways for tasks like patent chemistry searching and therapeutic intelligence analysis. The document advocates for making APIs more accessible to allow broader exploration of data that can uncover new use cases and insights, while also noting challenges around usability, data discovery, and security.
Doing more with less resources used to be a situation common just for academic scientists. This is unfortunately still true for academics but we are seeing others facing many of the same challenges. With the squeeze on budgets and cost cutting resulting from recent worldwide economic challenges, the failure of many drugs to make it through the pipeline to the market, and the increasing costs associated with the drug development process, we are now seeing in the pharmaceutical industry a dramatic shift, perhaps belatedly, to have to accommodate similar challenges of doing more with less
This document provides an overview of databases and tools relevant to systems immunology. It discusses several freely available and licensed databases containing gene expression, drug, pathway, and disease data. Issues with third party data like cleanup requirements and need for downloadability are also covered. Examples are given of integrating data from sources like GEO, DrugBank, Connectivity Map, and ImmPort to enable meta-analyses addressing immunological questions.
Exploring Chemical and Biological Knowledge Spaces with PubChemPaul Thiessen
My presentation for the Drug Repurposing workshop at the upcoming Bio-IT World Expo.
http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=124256
Presentation abstract:
PubChem has a wealth of chemical structure and biological activity information. In conjunction with NCBI’s other resources such as PubMed and GenBank, PubChem is a vast source of information relevant to repurposing not only of established drugs but any compounds with in vivo pharmacology and/or clinical results. The challenge is how to take advantage of this knowledge. The ability to explore not only chemical similarity but relationships between diseases and disease targets has crucial value in repurposing. While focused investigations are already possible within the existing Entrez system, navigation across these linked information spaces can be difficult to do on a large scale with current tools. We are actively developing new infrastructure to support such analyses, and pursuing new methods of exploring inter- and intra-database relationships between chemicals, targets, diseases, and patents. Progress and some future direction in these areas will be presented.
The internet continues to offer increased access to chemistry data that may be of value to scientists interested in populating systems containing reference toxicology data as well as to provide data for the development of predictive models. This presentation will give an overview of some of the various sources of data available via the internet, provide an overview of some of the challenges associated with gathering high-quality data and discuss methods by which to mesh together disparate data sources.
The document discusses strategies for efficiently designing "bio space" chemical libraries to identify drug molecules. It proposes using computational chemistry to integrate data from various R&D departments and design libraries with drug-like properties. Building blocks and scaffolds will be selected from existing drugs based on properties like absorption, distribution, metabolism, and excretion. This approach aims to design focused libraries more likely to yield meaningful hits in high-throughput screening that are inherently more drug-like.
HCL Technologies moved their website from ASP to Drupal in order to improve their online presence, user experience, and search functionality while reducing maintenance costs. They selected Drupal due to its abilities for content management, search engine optimization, and security through frequent updates as an open source platform. The transition faced challenges with content migration and performance optimization but provided benefits like enhanced content discovery, social sharing, and SEO friendliness. The project was successful and won an award with a small dedicated team.
Front end optimization is important because 80% of end-user response time is spent on the front-end and front-end optimization can cut page load times by 25-50%. Page load times significantly impact user experience and business metrics. Tools like Yslow and Google PageSpeed can help identify optimization opportunities. Image optimization, minimizing HTTP requests by combining files, and reducing payload sizes are some techniques that should be applied from the start of a project. Progressive page loading, splitting components across domains, browser caching, and preloading components can further improve performance.
2014-03-20 Open PHACTS - A Data Platform for Drug Discoveryopen_phacts
A data platform is proposed for drug discovery that would lower industry firewalls and enable pre-competitive data integration, analysis, and reuse across pharmaceutical companies. The platform would integrate external research data from literature, databases, and other sources on compounds, targets, pathways, and diseases. It would provide data integration and analysis tools through a firewalled database system and applications. The goal is to advance drug discovery by allowing multiple companies to access and build upon the same large foundation of pre-competitive research data.
This document discusses the challenges of integrating public domain drug discovery data from multiple sources and the mission of the Open PHACTS Foundation to address this issue. It outlines Open PHACTS' approach of integrating biomedical data resources into a single open access point using semantic web technologies and standards. This will allow users to perform queries across multiple data sources and analyze related data in an integrated manner. The document also notes some of the technical challenges around data integration due to differences in formats, identifiers and languages between sources.
Big data supporting drug discovery - cautionary tales from the world of chemi...Valery Tkachenko
The document discusses how big data and machine learning can support drug discovery efforts. It describes the Royal Society of Chemistry's (RSC) ChemSpider database, which contains over 30 million chemicals and is a central resource for searching chemical structures. The document outlines how RSC is working to improve data quality, enable semantic search of literature, and develop tools to help navigate the large chemical space in order to support drug discovery research. RSC is also working on data management, crowdsourcing, and building connections between databases to further advance chemistry research.
Domains such as drug discovery, data science, and policy studies increasing rely on the combination of complex analysis pipelines with integrated data sources to come to conclusions. A key question then arises is what are these conclusions based upon? Thus, there is a tension between integrating data for analysis and understanding where that data comes from (its provenance). In this talk, I describe recent work that is attempting to facilitate transparency by combining provenance tracked within databases with the data integration and analytics pipelines that feed them. I discuss this with respect to use cases from public policy as well as drug discovery.
Given at: http://ccct.uva.nl/content/ccct-seminar-21-february-2014
Slides to be presented at a webinar arranged by Metasolution as part of a Vinnova project http://metasolutions.se/2014/03/webbinarium-med-kerstin-forsberg-om-lankade-data-i-lakemedelsforskningen/
Data-driven drug discovery for rare diseases - Tales from the trenches (CINF ...Frederik van den Broek
Slides from my talk at the ACS CINF Symposium on Collaborations & Data Sharing in Rare & Orphan Disease Drug Discovery on 31 March 2019 in Orlando.
Abstract:
For the pharmaceutical industry as a whole, addressing the challenge of rare or orphan diseases is high on the agenda. But for the patients and their families, rare diseases can be very isolating and it can often feel like the potential for new treatments is low. One avenue for potential treatments is to identify drug repurposing candidates for the rare disease in question. This talk will give an overview of various collaborative projects undertaken in the last few years, which involved the combination, normalisation and analysis of data from various disparate sources, including some valuable lessons learnt along the way.
IUPHAR/BPS Guide to Pharmacology: concise mapping of chemistry, data, and tar...Chris Southan
The document summarizes the IUPHAR/BPS Guide to Pharmacology (GtoPdb) database, which maps relationships between chemistry, data, and protein targets. It has evolved from earlier databases to now include over 1500 human protein targets linked to ligand data. Challenges include resolving relationships across different target hierarchies and filling data gaps. Future plans include expanding the database and linking it to immunopharmacology data through a new Guide to Immunopharmacology portal.
With its focus on investigating the basis for the sustained existence
of living systems, modern biology has always been a fertile, if not
challenging, domain for formal knowledge representation and automated
reasoning. With thousands of databases and hundreds of ontologies now
available, there is a salient opportunity to integrate these for
discovery. In this talk, I will discuss our efforts to build a rich
foundational network of ontology-annotated linked data, develop
methods to intelligently retrieve content of interest, uncover
significant biological associations, and pursue new avenues for drug
discovery. As the portfolio of Semantic Web technologies continue to
mature in terms of functionality, scalability, and an understanding of
how to maximize their value, researchers will be strategically poised
to pursue increasingly sophisticated KR projects aimed at improving
our overall understanding of human health and disease.
bio: Dr. Michel Dumontier is an Associate Professor of Medicine
(Biomedical Informatics) at Stanford University. His research aims to
find new treatments for rare and complex diseases. His research
interest lie in the publication, integration, and discovery of
scientific knowledge. Dr. Dumontier serves as a co-chair for the World
Wide Web Consortium Semantic Web in Health Care and Life Sciences
Interest Group (W3C HCLSIG) and is the Scientific Director for
Bio2RDF, a widely used open-source project to create and provide
linked data for life sciences.
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...Frederik van den Broek
Slides from my talk at the ACS CINF Symposium on Chemical Nomenclature & Representation on 26 August 2019 in San Diego.
Abstract:
The first edition of the Beilstein Handbook of Organic Chemistry was published nearly 140 years ago. Electronic laboratory notebooks have been in use in chemistry for almost 20 years. And the life science industry still doesn't have a well-defined way of capturing and exchanging information about chemical reactions and relies on imprecise or vendor-specific data formats. Without a common language and structure to describe experiments, data integration is unnecessarily expensive and a significant part of published data has not been readily available for processing or analysis.
The Unified Data Model (UDM) project team aims to improve the situation. UDM is a collective effort of vendors and life science organizations to create an open, extendable and freely available reference model and data format for exchange of experimental information about compound synthesis and testing. Run under the umbrella of the Pistoia Alliance, the project team has published two releases of the UDM data format and it is expected that the model will continue to be improved as demand stipulates working with the Pistoia FAIR data implementation by industry community.
Curatorial data wrangling for the Guide to PHARMACOLGY Chris Southan
This document discusses the challenges and experiences of curating quantitative target-ligand interaction data for the Guide to PHARMACOLOGY database from the primary literature. Standardizing entities such as proteins, ligands, and measurement units across different data sources can be difficult due to inconsistencies in naming, identifiers, and reporting of values. Initiatives by publishers to have authors mark up key entities in manuscripts may help curators but will not solve all compatibility issues. The curation process also requires judgment calls on issues like resolving conflicts between data sources and determining whether reported data can be structurally defined.
This document discusses how APIs are enabling new ways of accessing and mashing up drug discovery data from various sources. It provides examples of existing bio/chem/med APIs and case studies of companies that are using APIs in novel ways for tasks like patent chemistry searching and therapeutic intelligence analysis. The document advocates for making APIs more accessible to allow broader exploration of data that can uncover new use cases and insights, while also noting challenges around usability, data discovery, and security.
Doing more with less resources used to be a situation common just for academic scientists. This is unfortunately still true for academics but we are seeing others facing many of the same challenges. With the squeeze on budgets and cost cutting resulting from recent worldwide economic challenges, the failure of many drugs to make it through the pipeline to the market, and the increasing costs associated with the drug development process, we are now seeing in the pharmaceutical industry a dramatic shift, perhaps belatedly, to have to accommodate similar challenges of doing more with less
This document provides an overview of databases and tools relevant to systems immunology. It discusses several freely available and licensed databases containing gene expression, drug, pathway, and disease data. Issues with third party data like cleanup requirements and need for downloadability are also covered. Examples are given of integrating data from sources like GEO, DrugBank, Connectivity Map, and ImmPort to enable meta-analyses addressing immunological questions.
Exploring Chemical and Biological Knowledge Spaces with PubChemPaul Thiessen
My presentation for the Drug Repurposing workshop at the upcoming Bio-IT World Expo.
http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=124256
Presentation abstract:
PubChem has a wealth of chemical structure and biological activity information. In conjunction with NCBI’s other resources such as PubMed and GenBank, PubChem is a vast source of information relevant to repurposing not only of established drugs but any compounds with in vivo pharmacology and/or clinical results. The challenge is how to take advantage of this knowledge. The ability to explore not only chemical similarity but relationships between diseases and disease targets has crucial value in repurposing. While focused investigations are already possible within the existing Entrez system, navigation across these linked information spaces can be difficult to do on a large scale with current tools. We are actively developing new infrastructure to support such analyses, and pursuing new methods of exploring inter- and intra-database relationships between chemicals, targets, diseases, and patents. Progress and some future direction in these areas will be presented.
The internet continues to offer increased access to chemistry data that may be of value to scientists interested in populating systems containing reference toxicology data as well as to provide data for the development of predictive models. This presentation will give an overview of some of the various sources of data available via the internet, provide an overview of some of the challenges associated with gathering high-quality data and discuss methods by which to mesh together disparate data sources.
The document discusses strategies for efficiently designing "bio space" chemical libraries to identify drug molecules. It proposes using computational chemistry to integrate data from various R&D departments and design libraries with drug-like properties. Building blocks and scaffolds will be selected from existing drugs based on properties like absorption, distribution, metabolism, and excretion. This approach aims to design focused libraries more likely to yield meaningful hits in high-throughput screening that are inherently more drug-like.
HCL Technologies moved their website from ASP to Drupal in order to improve their online presence, user experience, and search functionality while reducing maintenance costs. They selected Drupal due to its abilities for content management, search engine optimization, and security through frequent updates as an open source platform. The transition faced challenges with content migration and performance optimization but provided benefits like enhanced content discovery, social sharing, and SEO friendliness. The project was successful and won an award with a small dedicated team.
Front end optimization is important because 80% of end-user response time is spent on the front-end and front-end optimization can cut page load times by 25-50%. Page load times significantly impact user experience and business metrics. Tools like Yslow and Google PageSpeed can help identify optimization opportunities. Image optimization, minimizing HTTP requests by combining files, and reducing payload sizes are some techniques that should be applied from the start of a project. Progressive page loading, splitting components across domains, browser caching, and preloading components can further improve performance.
Front-end performance optimizing involves optimizing a website's HTML, CSS, JavaScript, and image files to achieve the fastest possible loading speed. This includes minimizing HTTP requests by combining files, compressing files, optimizing code by removing unused code and errors, leveraging browser caching, and parallelizing downloads across domains. The document outlines nine techniques for front-end optimization, such as optimizing file sizes, reducing download size through compression and caching, and minimizing HTTP requests through file combining and CSS sprites.
Yahoo has developed the de facto standard for building fast front-ends for websites. The bad news: you have to follow 34 rules to get there. The good news: I'll take a subset of those rules, explain them, and show how you can implement those rules in an automated fashion to minimize impact on developers and designers for your high-traffic website.
UXify 2015 - Front-end Developers' Checklist for Better UXStoian Dipchikov
Good UX has always been one of the key factors for success in the contemporary web development and there fore has led to huge improvements in our industry in the last years. Nowadays the UX of a software product is not responsibility only of the UX Architects / Producers, but to each individual involved in the creation of an app or a website, including the Front-end developers.
The talk presents a summarized list of DOs and DON’Ts, which Stoyan and his team believe should be respected by the Front-end developers if they want to build a useable web product, up to and above industry standards. There will be a lot of case studies and actual examples taken from Despark’s experience in the field.
Opening up pharmacological space, the OPEN PHACTs apiChris Evelo
The document provides an overview of the Open PHACTS project, which aims to create an open pharmacological space (OPS) through semantic integration of public drug discovery resources. It discusses the challenges of accessing and integrating scientific data across organizational boundaries. Open PHACTS builds a service layer and applications to allow standardized access and analysis of data from various public sources. It is a collaborative project involving academic and industry partners seeking to make pre-competitive drug discovery data more accessible and useful through semantic integration and common standards.
Open PHACTS (Sept 2013) EBI Industry ProgrammeSciBite Limited
This is a talk i'm giving for the EBI's industry programme on 18th Sept. The slides are mostly what you may have seen before with a few new ones thrown in to not make it too boring! With grateful acknowledgment of all the folks in Open PHACTS who make this stuff happen.
BDE SC1 Workshop 3 - Open PHACTS Pilot (Kiera McNeice)BigData_Europe
Overview of Open PHACTS, the BDE Pilot project in SC1, presented at BDE SC1 Workshop 3, 13 December, 2017.
https://www.big-data-europe.eu/the-final-big-data-europe-workshop/
Current advances to bridge the usability-expressivity gap in biomedical seman...Maulik Kamdar
I presented a talk at the Protege research meeting on the 'Current advances to bridge the usability-expressivity gap in biomedical semantic search (and visualizing linked data)' https://sites.google.com/site/protegeresearchmeeting/meeting-materials/current-advances-to-bridge-the-usability-expressivity-gap-in-semantic-search
Stephen Friend Dana Farber Cancer Institute 2011-10-24Sage Base
The document discusses building disease models using data intensive science and open medical information systems, with the goal of better understanding disease biology before testing drugs. It describes the Sage Bionetworks non-profit organization, which aims to create a commons for shared disease maps and models through several pilot projects including clinical trial data sharing and identifying cancer patients who do not respond to approved drug regimens.
1) Quantitative medicine uses large amounts of medical data and advanced analytics to determine the most effective treatment for individual patients based on their specific clinical profile and biomarkers. This approach can help reduce healthcare costs and improve outcomes compared to the traditional one-size-fits-all model.
2) However, realizing the promise of quantitative personalized medicine is challenging due to the huge quantities of diverse medical data located in dispersed systems, lack of computing capabilities, and barriers to data sharing.
3) Grid and service-oriented computing approaches are helping to address these challenges by enabling federated querying, analysis, and sharing of medical data and services across organizations through virtual integration rather than true consolidation.
Introduction to Jackson Labs, JMCRS, Clinical Services and Scientific Services at the Jackson Labs. Differences between long and short read sequencing. FAIR Data Action Plan. Metadata needs. Data Commons and the need to capture sample specific gene models discovered.
This document describes the CompTox Chemistry Dashboard, a publicly accessible website developed by the EPA's National Center for Computational Toxicology that provides data on over 762,000 chemicals. The dashboard contains experimental and predicted physicochemical property data, environmental fate and transport data, toxicity data, and models. It allows users to search, view detailed chemical pages, access prediction reports, perform batch searches, and will integrate additional predicted properties and data in the future. The goal is to provide a central resource for computational toxicology data to support chemical safety assessments.
This document discusses next generation sequencing (NGS) data and implications for data stewardship. It notes that NGS allows measuring the full-length transcriptome, including alternatively spliced transcripts specific to samples. This alters gene models and highlights the need to capture gene models and context in data commons for future reuse. The document also recommends that more metadata be captured about samples, experiments, and instruments to provide context and aid in data processing. It emphasizes making data FAIR (findable, accessible, interoperable, and reusable) according to W3C standards to improve data stewardship and enable both human and machine use of data.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
1. The document summarizes Kerstin Forsberg's presentation on semantics and linked data at AstraZeneca R&D. It discusses (1) an internal competitive intelligence tool called CI360, (2) public pre-competitive projects like Open PHACTS and standards bodies, (3) AstraZeneca's Linked Data Community of Practice, and (4) ongoing work on study identifiers and APIs.
2. It provides an overview of Kerstin Forsberg's background and goal of improving the utility of clinical trial data through semantic interoperability. It also outlines some of AstraZeneca's collaborations and contributions to linked data initiatives.
3. The presentation highlights AstraZeneca
This document discusses the Collaborative Drug Discovery (CDD) platform, which aims to facilitate drug discovery collaborations through secure data sharing. Key points:
- CDD provides a secure web-based platform (CDD Vault) for researchers to store private data and selectively share subsets with collaborators. It also hosts over 3 million public compounds.
- The platform allows users to simultaneously query private, collaborator, and public data. It has been used by thousands of scientists for projects like accelerating tuberculosis drug discovery.
- Analysis of data contributions to the platform found it follows a power law distribution, indicating most users only contribute a small amount but a long tail of more engaged users help maximize data sharing
The Open PHACTS project delivers an online platform integrating a wide variety of data from across chemistry and the life sciences and an ecosystem of tools and services to query this data in support of pharmacological research, turning the semantic web from a research project into something that can be used by practising medicinal chemists in both academia and industry. In the summer of 2015 it was the first winner of the European Linked Data Award. At the Royal Society of Chemistry we have provided the chemical underpinnings to this system and in this talk we review its development over the past five years. We cover both our early work on semantic modelling of chemistry data for the Open PHACTS triplestore and more recent work building an all-purpose data platform, for which the Open PHACTS data has been an important test case, what has worked well, what's missing and where this is is likely to go in future.
Predicting Drug Candidates Safety : the Role and Usage of Knowledge BasesAureus Sciences
- Aureus Sciences builds knowledge bases for predicting drug candidates' safety, focusing on areas like drug-drug interactions, safety pharmacology, and off-target effects.
- They have developed large structured databases of chemical and bioactivity information from literature and provide applications and services to analyze the data for customers in drug development.
- Their predictive models and databases have been shown to accurately predict drug interactions and off-target effects, helping customers optimize drug safety assessment.
Stratergies for the intergration of information (IPI_ConfEX)Ben Gardner
The document discusses approaches to integrating internal and external data across pharmaceutical research. It describes utilizing a data warehousing strategy through a Research Information Factory (RIF) to create a single global repository for research data. However, integrating external data from various sources poses additional challenges. Tools like PharmaMatrix provide a pre-indexed mine of scientific literature linking drug targets to indications, but result sets can be large. The document suggests that Web 2.0 technologies like wikis, blogs and tagging could help turn integrated information into knowledge by enabling collaboration and sharing. Industry-wide data standards and common ontologies would also help facilitate external data integration.
ReVeaLD: A user-driven domain-specific interactive search platform for biomed...Maulik Kamdar
1) ReVeaLD is an interactive search platform that allows biomedical researchers to query linked open data sources using natural language queries or a visual interface.
2) It addresses challenges of accessing heterogeneous biomedical data sources by providing a domain-specific language and query templates to form SPARQL queries for multiple data sources.
3) The system was evaluated on tasks involving formulating queries using the domain-specific language concepts and linked open data catalog, and it was found that familiarity with the domain-specific language concepts and a smaller set of concepts improved query formulation times.
This document discusses using semantic web technologies for translational research in life sciences. It provides an overview of semantic web standards and outlines several projects demonstrating applications in healthcare and biomedical research. These include developing an active semantic electronic medical record, semantically annotating experimental glycomics data, and integrating diverse biomedical data sources using ontologies to enable complex querying and knowledge discovery.
Next-Gen Drug Discovery: An Integrated Micro-Droplet Based PlatformLaura Berry
Presented at the Global Medicinal Chemistry and GPCR Summit. To find out more, visit:
www.global-engage.com
Alexander Alanine, CEO of Bacteva, introduces the Totally Integrated Medicines Engine (TIME), designed to speed up drug discovery with integrated microfluidics.
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Open PHACTS April 2017 Science webinar Workflow toolsopen_phacts
This webinar discusses workflow tools to support life science research. It includes presentations on the Common Workflow Language (CWL) by Michael Crusoe and uses of Knime and Pipeline Pilot workflows with Open PHACTS examples. There will also be a panel discussion on the future of workflows for life science research with speakers from Eli Lilly, Janssen, and others. Example CWL workflows are shown to demonstrate portable life science workflows.
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2015-04-28 Open PHACTS at Swedish Linked Data Network Meet-up
1. The Open PHACTS Discovery
Platform
Semantic Data Integration for Life Sciences
Ola Engkvist Discovery Sciences, AstraZeneca R&D Mölndal
Swedish Linked Data Network Meet-up 2015-04-28
4. Pre-competitive Informatics:
Pharma are all accessing, processing, storing & re-processing external research data
Literature
PubChem
Genbank
Patents
Databases
Downloads
Data Integration Data Analysis
Firewalled Databases
Repeat @
each
company
x
Lowering industry firewalls: pre-competitive informatics in drug discovery
Nature Reviews Drug Discovery (2009) 8, 701-708 doi:10.1038/nrd2944
5. Over the last decade
• Data has become more open
• Data has become better represented (Standards)
• Major providers are becoming more organised (NCBI, EBI, FDA)
BUT
Integration across sources, and across providers is
still a gap
6. • EC funded public-private
partnership for
pharmaceutical research
• Focus on key problems
– Efficacy, Safety,
Education & Training,
Knowledge
Management
The Innovative Medicines Initiative
The Open PHACTS Project
• Create a semantic integration hub (“Open
Pharmacological Space”)…
• Runs 2011-2014, ENSO till 2016
• Deliver services to support on-going drug
discovery programs in pharma and public domain
• Leading academics in semantics, pharmacology
and informatics, driven by solid industry business
requirements
• 15 academic partners, 10 pharmaceutical
companies, 6 SMEs
• Work split into clusters:
• Technical Build
• Scientific Drive
• Community & Sustainability
9. Number sum Nr of 1 Question
15 12 9 All oxidoreductase inhibitors active <100nM in both human and mouse
18 14 8
Given compound X, what is its predicted secondary pharmacology? What are the on and
off,target safety concerns for a compound? What is the evidence and how reliable is that
evidence (journal impact factor, KOL) for findings associated with a compound?
24 13 8
Given a target find me all actives against that target. Find/predict polypharmacology of actives.
Determine ADMET profile of actives.
32 13 8 For a given interaction profile, give me compounds similar to it.
37 13 8
The current Factor Xa lead series is characterised by substructure X. Retrieve all bioactivity data
in serine protease assays for molecules that contain substructure X.
38 13 8
Retrieve all experimental and clinical data for a given list of compounds defined by their chemical
structure (with options to match stereochemistry or not).
41 13 8
A project is considering Protein Kinase C Alpha (PRKCA) as a target. What are all the
compounds known to modulate the target directly? What are the compounds that may modulate
the target directly? i.e. return all cmpds active in assays where the resolution is at least at the
level of the target family (i.e. PKC) both from structured assay databases and the literature.
44 13 8 Give me all active compounds on a given target with the relevant assay data
46 13 8
Give me the compound(s) which hit most specifically the multiple targets in a given pathway
(disease)
59 14 8 Identify all known protein-protein interaction inhibitors
Business Question Driven Approach
10. http://dx.doi.org/10.1016/j.websem.2014.03.003
The Open PHACTS Discovery Platform
• Cloud-Based
“Production” Level
System. Secure & Private
• Guided By Business
Questions
• Uses Semantic Web
Technology But provides
a simple REST-ful API for
everyone else
http://dx.doi.org/10.1016/j.drudis.2013.05.008
14. Nanopub
Db
VoID
Data Cache
(Virtuoso Triple Store)
Semantic Workflow Engine
Linked Data API (RDF/XML, TTL, JSON)
Domain
Specific
Services
Identity
Resolution
Service
Chemistry
Registration
Normalisation
& Q/C
Identifier
Management
Service
Indexing
CorePlatform
P12374
EC2.43.4
CS4532
“Adenosine
receptor 2a”
VoID
Db
Nanopub
Db
VoID
Db
VoID
Nanopub
VoID
Public Content Commercial
Public Ontologies
User
Annotations
Apps
25. Integration of Open PHACTS data with internal experimental data
Tom Plasterer, Ola Engkvist & Discovery Sciences, Chemical Biology
26. Analyzing of high-throughput screening data
Linda Zander-Balderud, Peter Varkonyi, Isabella Feierberg
Bio-Assay
Ontology
AllegroGraph
Annotation Sparql
Removal of “frequent hitters”
27. info@openphactsfoundation.org @Open_PHACTS
Open PHACTS Practical Semantics
bryn@openphactsfoundation.org
Acknowledgements
GlaxoSmithKline – Coordinator
Universität Wien – Managing entity
Technical University of Denmark
University of Hamburg, Center for
Bioinformatics
BioSolveIT GmBH
Consorci Mar Parc de Salut de Barcelona
Leiden University Medical Centre
Royal Society of Chemistry
Vrije Universiteit Amsterdam
Novartis
Merck Serono
H. Lundbeck A/S
Eli Lilly
Netherlands Bioinformatics Centre
Swiss Institute of Bioinformatics
ConnectedDiscovery
EMBL-European Bioinformatics Institute
Janssen Esteve Almirall
OpenLink Scibite
The Open PHACTS Foundation
Spanish National Cancer Research Centre
University of Manchester
Maastricht University
Aqnowledge
University of Santiago de Compostela
Rheinische Friedrich-Wilhelms-Universität
Bonn
AstraZeneca
Pfizer
Editor's Notes
10’
Mx/psa, how calculated who did it?
Mash up. With your data too,
- top layer join together but need them all
commerical
10
Can go get everything
OPS not a repo of the world, specific sources