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.
GtoPdb: A resource for cell-based perturbogensChris Southan
Poster for ELRIG, Möndal, 11/12 May 2017.
This poster will also be presented at BioITWorld, Boston, May 23-25
A resource for the selection and interpretation of cell-based perturbogens: the IUPHAR/BPS Guide to PHARMACOLOGY
Christopher Southan, Elena Faccenda, Joanna L. Sharman, Adam J. Pawson, Simon D. Harding, Jamie A Davies,
Translational research requires the integration of the in vitro molecular mechanisms of action (mmoa) of small molecules, cell-based screening studies, animal models and eventual clinical trials. The International Union of Pharmacology (IUPHAR)/British Pharmacology Society (BPS) database, GtoPdb http://www.guidetopharmacology.org/ provides expert-annotated molecular interactions between endogenous receptor ligands, probes, lead compounds, clinical drugs and their protein targets. It thus provides a core set of quantitative pharmacological relationships that can be interrogated for many purposes, including those running cell-based screens, not only during result interpretation but also to identify key compounds for scoping and consolidation experiments. As described in [1] GtoPdb is populated by records extracted from pharmacology and medicinal chemistry journals, and released quarterly. Quality is ensured by curatorial stringency and our unique model of content selection based on recommendations from IUPHAR target class subcommittees of international experts collaborating with the in-house curators. The database now has over 14 000 binding values (mainly IC50, Ki or Kd) between 8000 ligands and 15000 human proteins (mainly primary but also secondary off-target interactions) representing a 7% druggable proteome. Our coverage is complementary to other sources. For example the 6565 structures we recently submitted to PubChem as CIDs, 5206 were not in DrugBank and 1535 not in ChEMBL. This includes recommended tool compounds with relatively defined mmoa (including 110 from the Structural Genomics Consortium Probe Portal). We also have 75% overlap with vendors for procurement and 80% with patent extractions that in many cases allow mapping to SAR data sets from first-filings (some of which we point to). In a cell screening context 1254 of our targets intersect with proteins in the Reactome pathway database. This is one way to select chemical peturbation points that could be detected by assay readouts. From Nov 2015 we have been funded by the Wellcome Trust to extend into immunopharmacology (within the existing database schema) that is now driving overall GtoPdb content expansion. Parties engaged in cell based assays using or could use compounds we have are encouraged to use GtoPdb, contact us for queries, possible analogue expansions and/or alert us to prospective new content. [1] Southan C et. al. (2016) Nucleic Acids Res. 44(D1):D1054-68, PMID: 26464438
Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
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.
Search Interface Feature Evaluation in BiosciencesZanda Mark
Read more here: http://pingar.com/
This paper reports findings on desirable interface features for different
search tasks in the biomedical domain. We conducted a user study where
we asked bioscientists to evaluate the usefulness of autocomplete, query
expansions, faceted refinement, related searches and results preview
implementations in new pilot interfaces and publicly available systems
while using baseline and their own queries. Our evaluation reveals that
there is a preference for certain features depending on the search task.
In addition, we touch on the current pain point of faceted search: the
acquisition of faceted subject metadata for unstructured documents.
We found a strong preference for prototypes displaying just a few facets
generated based on either the query or the matching documents.
Our evaluation reveals that there is a preference for certain features depending on the search task. In addition, we touch on the current pain point of faceted search: the acquisition of faceted subject metadata for unstructured documents. We found a strong preference for prototypes displaying just a few facets generated based on either the query or the matching documents.
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.
GtoPdb: A resource for cell-based perturbogensChris Southan
Poster for ELRIG, Möndal, 11/12 May 2017.
This poster will also be presented at BioITWorld, Boston, May 23-25
A resource for the selection and interpretation of cell-based perturbogens: the IUPHAR/BPS Guide to PHARMACOLOGY
Christopher Southan, Elena Faccenda, Joanna L. Sharman, Adam J. Pawson, Simon D. Harding, Jamie A Davies,
Translational research requires the integration of the in vitro molecular mechanisms of action (mmoa) of small molecules, cell-based screening studies, animal models and eventual clinical trials. The International Union of Pharmacology (IUPHAR)/British Pharmacology Society (BPS) database, GtoPdb http://www.guidetopharmacology.org/ provides expert-annotated molecular interactions between endogenous receptor ligands, probes, lead compounds, clinical drugs and their protein targets. It thus provides a core set of quantitative pharmacological relationships that can be interrogated for many purposes, including those running cell-based screens, not only during result interpretation but also to identify key compounds for scoping and consolidation experiments. As described in [1] GtoPdb is populated by records extracted from pharmacology and medicinal chemistry journals, and released quarterly. Quality is ensured by curatorial stringency and our unique model of content selection based on recommendations from IUPHAR target class subcommittees of international experts collaborating with the in-house curators. The database now has over 14 000 binding values (mainly IC50, Ki or Kd) between 8000 ligands and 15000 human proteins (mainly primary but also secondary off-target interactions) representing a 7% druggable proteome. Our coverage is complementary to other sources. For example the 6565 structures we recently submitted to PubChem as CIDs, 5206 were not in DrugBank and 1535 not in ChEMBL. This includes recommended tool compounds with relatively defined mmoa (including 110 from the Structural Genomics Consortium Probe Portal). We also have 75% overlap with vendors for procurement and 80% with patent extractions that in many cases allow mapping to SAR data sets from first-filings (some of which we point to). In a cell screening context 1254 of our targets intersect with proteins in the Reactome pathway database. This is one way to select chemical peturbation points that could be detected by assay readouts. From Nov 2015 we have been funded by the Wellcome Trust to extend into immunopharmacology (within the existing database schema) that is now driving overall GtoPdb content expansion. Parties engaged in cell based assays using or could use compounds we have are encouraged to use GtoPdb, contact us for queries, possible analogue expansions and/or alert us to prospective new content. [1] Southan C et. al. (2016) Nucleic Acids Res. 44(D1):D1054-68, PMID: 26464438
Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
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.
Search Interface Feature Evaluation in BiosciencesZanda Mark
Read more here: http://pingar.com/
This paper reports findings on desirable interface features for different
search tasks in the biomedical domain. We conducted a user study where
we asked bioscientists to evaluate the usefulness of autocomplete, query
expansions, faceted refinement, related searches and results preview
implementations in new pilot interfaces and publicly available systems
while using baseline and their own queries. Our evaluation reveals that
there is a preference for certain features depending on the search task.
In addition, we touch on the current pain point of faceted search: the
acquisition of faceted subject metadata for unstructured documents.
We found a strong preference for prototypes displaying just a few facets
generated based on either the query or the matching documents.
Our evaluation reveals that there is a preference for certain features depending on the search task. In addition, we touch on the current pain point of faceted search: the acquisition of faceted subject metadata for unstructured documents. We found a strong preference for prototypes displaying just a few facets generated based on either the query or the matching documents.
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.
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.
Metabolomic data analysis and visualization toolsDmitry Grapov
This document discusses tools and methods for metabolomic data analysis and visualization. It covers visualization techniques like plots and networks to explore patterns in data. It also discusses statistical analysis methods like ANOVA and clustering for significance testing and pattern detection. Additionally, it discusses predictive modeling, network analysis using pathways, and network mapping to relate metabolites based on biochemical transformations, structural similarity, or empirical dependencies. Common analysis tasks and featured open-source tools are also highlighted.
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.
Complex Systems Biology Informed Data Analysis and Machine LearningDmitry Grapov
Dmitry Grapov is a data scientist and principal statistician at the NIH West Coast Metabolomics Center. He received his PhD in analytical chemistry from the University of California, Davis and has applied complex systems biology, data analysis, and machine learning techniques to problems in predictive modeling, biomarker discovery, and personalized medicine. He has developed software tools like DeviumWeb and MetaMapR to integrate multi-omic datasets and build biochemical networks for applications in systems biology and wellness optimization.
This document discusses databases that define the druggable proteome - the portion of the human proteome that can bind small molecules with sufficient affinity for modulating protein function. Four databases - ChEMBL, BindingDB, DrugBank, and IUPHAR/BPS Guide to PHARMACOLOGY - provide evidence-supported links between human proteins and drug targets. Their intersection identifies ~490 proteins (13% of the union of targets) as the most precisely defined druggable proteome. Comparative analyses examine distributions of targets by function and other attributes. Initiatives aim to expand knowledge of currently unannotated but potentially druggable protein families to broaden therapeutic opportunities.
Collaboraive sharing of molecules and data in the mobile ageSean Ekins
The document discusses collaborative drug discovery and the use of mobile applications in chemistry. It describes how the Collaborative Drug Discovery (CDD) platform allows researchers to securely share molecules and data. Examples are provided of collaborations between academic labs and pharmaceutical companies using the CDD vault to work on projects related to tuberculosis drug development. The rise of mobile devices is creating new opportunities for chemistry applications to enable collaborative workflows involving tasks like structure drawing, database searching, and data sharing from any location.
Acs collaborative computational technologies for biomedical research an enabl...Sean Ekins
This document discusses enabling more open and collaborative approaches to drug discovery through computational technologies. It argues that pre-competitive data sharing could help integrate historical knowledge and deliver high value. Open drug discovery may be a better approach than the traditional closed model. Tools and open interfaces could facilitate more open collaboration between different sectors involved in biomedical research. Mobile apps may help scientists access and share data more easily. Crowdsourcing approaches could engage more contributors to knowledge bases.
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
Virtual Screening and Hit PrioritizationPuneet Kacker
This document discusses virtual ligand screening (VLS) as an alternative to high-throughput screening for identifying potential drug candidates. It describes the VLS process, which involves selecting a target and compound library, preparing the target and ligands, running a docking simulation to analyze ligand-target binding, and prioritizing hits. The document outlines advantages of computational methods like VLS compared to experimental screening, as well as some limitations. It also provides examples of free and commercial docking engines that can be used and highlights challenges in VLS like accounting for receptor flexibility.
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.
Poster titled "The imperative of small, high quality data for underpinning big data: the IUPHAR/BPS Guide to PHARMACOLOGY". Presented by Dr. Christopher Southan, at the British Society of Pharmacology, Institute for Translational Medicine & Therapeutics (ITMAT) Meeting, Edinburgh, March 2017, ‘Big Data & the Development of New Medicines’.
Guide to PHARMACOLOGY: a web-Based Compendium for Research and EducationChris Southan
This document summarizes a presentation about the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) database. The following key points are made:
- GtoPdb is an online resource containing information on over 8,000 ligands and their interactions with around 1,500 human protein targets. It has been used widely by researchers and educators since 2009.
- The database contains detailed information on drug targets like GPCRs, ion channels, and enzymes. It also provides data on ligands, drugs, interactions between ligands and targets, and related clinical information.
- Users can browse targets and ligands or search the database. Detailed target pages contain pharmacology data, mechanisms, and links
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
This document provides information about analyzing next generation sequencing (NGS) data in Pathway Studio, including both RNA-Seq and variant analysis capabilities. It describes how to import RNA-Seq and genomic variant data files, perform targeted searches of the dbSNP database, compare variants across multiple genomes, and find variants associated with specific diseases or cellular processes. Examples of biological queries are also provided, such as searching for novel damaging variants in apoptosis-related genes or homozygous variants present in breast cancer cases but not controls. Help resources for NGS analysis in Pathway Studio are identified.
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.
Connecting Metabolomic Data with ContextDmitry Grapov
The document summarizes Dmitry Grapov's presentation on connecting metabolomic data with context. It discusses using network mapping and multivariate tools to analyze metabolomic data by generating connections between metabolites based on biochemical, chemical, and empirical relationships. These connections can help identify relationships between experimental observations and link the known with unknown. The presentation also provides examples of projects applying these techniques to analyze data from various disease studies involving changes in lipids, proteins, and small molecule metabolites.
The Next Generation Open Targets PlatformHelenaCornu
The next-generation version of the Open Targets Platform — the culmination of two years of work — is now officially live! It replaces our previous version, with a fresh new look, brand new features, and streamlined processes.
It is available at platform.opentargets.org
This presentation goes through the main changes to the Platform, and introduces the new Open Targets Community forum. Join now at community.opentargets.org.
Open Targets is an innovative, large-scale, multi-year, public-private partnership that uses human genetics and genomics data for systematic drug target identification and prioritisation. Find out more at opentargets.org
Scholarly Communication for Bioinformatics StudentsPhilip Bourne
Presentation made to the incoming bioinformatics and systems biology students at UCSD on how they could get involved in changing scholarly communication. Given February 28, 2011
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.
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.
Metabolomic data analysis and visualization toolsDmitry Grapov
This document discusses tools and methods for metabolomic data analysis and visualization. It covers visualization techniques like plots and networks to explore patterns in data. It also discusses statistical analysis methods like ANOVA and clustering for significance testing and pattern detection. Additionally, it discusses predictive modeling, network analysis using pathways, and network mapping to relate metabolites based on biochemical transformations, structural similarity, or empirical dependencies. Common analysis tasks and featured open-source tools are also highlighted.
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.
Complex Systems Biology Informed Data Analysis and Machine LearningDmitry Grapov
Dmitry Grapov is a data scientist and principal statistician at the NIH West Coast Metabolomics Center. He received his PhD in analytical chemistry from the University of California, Davis and has applied complex systems biology, data analysis, and machine learning techniques to problems in predictive modeling, biomarker discovery, and personalized medicine. He has developed software tools like DeviumWeb and MetaMapR to integrate multi-omic datasets and build biochemical networks for applications in systems biology and wellness optimization.
This document discusses databases that define the druggable proteome - the portion of the human proteome that can bind small molecules with sufficient affinity for modulating protein function. Four databases - ChEMBL, BindingDB, DrugBank, and IUPHAR/BPS Guide to PHARMACOLOGY - provide evidence-supported links between human proteins and drug targets. Their intersection identifies ~490 proteins (13% of the union of targets) as the most precisely defined druggable proteome. Comparative analyses examine distributions of targets by function and other attributes. Initiatives aim to expand knowledge of currently unannotated but potentially druggable protein families to broaden therapeutic opportunities.
Collaboraive sharing of molecules and data in the mobile ageSean Ekins
The document discusses collaborative drug discovery and the use of mobile applications in chemistry. It describes how the Collaborative Drug Discovery (CDD) platform allows researchers to securely share molecules and data. Examples are provided of collaborations between academic labs and pharmaceutical companies using the CDD vault to work on projects related to tuberculosis drug development. The rise of mobile devices is creating new opportunities for chemistry applications to enable collaborative workflows involving tasks like structure drawing, database searching, and data sharing from any location.
Acs collaborative computational technologies for biomedical research an enabl...Sean Ekins
This document discusses enabling more open and collaborative approaches to drug discovery through computational technologies. It argues that pre-competitive data sharing could help integrate historical knowledge and deliver high value. Open drug discovery may be a better approach than the traditional closed model. Tools and open interfaces could facilitate more open collaboration between different sectors involved in biomedical research. Mobile apps may help scientists access and share data more easily. Crowdsourcing approaches could engage more contributors to knowledge bases.
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
Virtual Screening and Hit PrioritizationPuneet Kacker
This document discusses virtual ligand screening (VLS) as an alternative to high-throughput screening for identifying potential drug candidates. It describes the VLS process, which involves selecting a target and compound library, preparing the target and ligands, running a docking simulation to analyze ligand-target binding, and prioritizing hits. The document outlines advantages of computational methods like VLS compared to experimental screening, as well as some limitations. It also provides examples of free and commercial docking engines that can be used and highlights challenges in VLS like accounting for receptor flexibility.
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.
Poster titled "The imperative of small, high quality data for underpinning big data: the IUPHAR/BPS Guide to PHARMACOLOGY". Presented by Dr. Christopher Southan, at the British Society of Pharmacology, Institute for Translational Medicine & Therapeutics (ITMAT) Meeting, Edinburgh, March 2017, ‘Big Data & the Development of New Medicines’.
Guide to PHARMACOLOGY: a web-Based Compendium for Research and EducationChris Southan
This document summarizes a presentation about the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) database. The following key points are made:
- GtoPdb is an online resource containing information on over 8,000 ligands and their interactions with around 1,500 human protein targets. It has been used widely by researchers and educators since 2009.
- The database contains detailed information on drug targets like GPCRs, ion channels, and enzymes. It also provides data on ligands, drugs, interactions between ligands and targets, and related clinical information.
- Users can browse targets and ligands or search the database. Detailed target pages contain pharmacology data, mechanisms, and links
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
This document provides information about analyzing next generation sequencing (NGS) data in Pathway Studio, including both RNA-Seq and variant analysis capabilities. It describes how to import RNA-Seq and genomic variant data files, perform targeted searches of the dbSNP database, compare variants across multiple genomes, and find variants associated with specific diseases or cellular processes. Examples of biological queries are also provided, such as searching for novel damaging variants in apoptosis-related genes or homozygous variants present in breast cancer cases but not controls. Help resources for NGS analysis in Pathway Studio are identified.
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.
Connecting Metabolomic Data with ContextDmitry Grapov
The document summarizes Dmitry Grapov's presentation on connecting metabolomic data with context. It discusses using network mapping and multivariate tools to analyze metabolomic data by generating connections between metabolites based on biochemical, chemical, and empirical relationships. These connections can help identify relationships between experimental observations and link the known with unknown. The presentation also provides examples of projects applying these techniques to analyze data from various disease studies involving changes in lipids, proteins, and small molecule metabolites.
The Next Generation Open Targets PlatformHelenaCornu
The next-generation version of the Open Targets Platform — the culmination of two years of work — is now officially live! It replaces our previous version, with a fresh new look, brand new features, and streamlined processes.
It is available at platform.opentargets.org
This presentation goes through the main changes to the Platform, and introduces the new Open Targets Community forum. Join now at community.opentargets.org.
Open Targets is an innovative, large-scale, multi-year, public-private partnership that uses human genetics and genomics data for systematic drug target identification and prioritisation. Find out more at opentargets.org
Scholarly Communication for Bioinformatics StudentsPhilip Bourne
Presentation made to the incoming bioinformatics and systems biology students at UCSD on how they could get involved in changing scholarly communication. Given February 28, 2011
Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.
IRJET - Deep Collaborrative Filtering with Aspect InformationIRJET Journal
This document discusses a proposed system for deep collaborative filtering with aspect information. The system aims to help web users efficiently locate relevant information on unfamiliar topics to increase their knowledge. It utilizes techniques like multi-keyword search, synonym matching, and ontology mapping to return relevant web links, images, and news articles to the user based on their search terms. The proposed system architecture includes an index structure to efficiently search and rank results based on similarity to the search query terms. The implementation and evaluation of the proposed system are also discussed.
This document presents the ligand-binders ontology developed to standardize information about protein binders and their targets. The ontology defines concepts like binders, targets, experiments, and their properties and relationships. It represents key concepts through classes and subclasses, and defines class properties to describe concepts. The ontology aims to provide a standardized vocabulary and enable reasoning about protein binder technologies. It was developed based on domain expertise and minimal information needed to describe binder-target pairs.
The document describes Carlos Manuel Estévez-Bretón's doctoral research on functionally characterizing metabolic networks. The goals are to classify metabolic pathways based solely on their functional features using machine learning methods, develop a system for functionally representing metabolic networks, and apply machine learning methods to study systems biology in new ways. The methodology involves using data from MetaCyc and KEGG databases, developing a functional representation model, classifying networks with supervised and unsupervised machine learning methods, and evaluating the results using various metrics.
This document presents a framework for reusing existing software agents through ontological engineering. The framework includes components like a user interface agent, query processor, mapping agent, transfer agent, wrapper agent, and remote agents containing ontologies. The query processor reformulates the user's query, the mapping agent identifies relevant ontologies, and the transfer agent sends the query to remote agents. The remote agents provide ontologies as output, which are then integrated/merged and presented back to the user interface agent. The goal is to enable reuse of heterogeneous agents across different development environments through a standardized ontology representation.
Developing Frameworks and Tools for Animal Trait Ontology (ATO) Jie Bao
This document discusses the development of software tools to facilitate the creation, editing, curation, and management of animal trait ontologies (ATOs). The authors have developed an ATO editor that allows multiple users to collaboratively work on an ontology simultaneously without overwriting each other. They have also developed database structures to manage trait ontologies for various livestock species. To date, over 300 pig traits have been included in the ontology database. The goal is for trait ontologies to be developed and maintained by livestock research communities through these collaborative tools.
Finding common ground: integrating the eagle-i and VIVO ontologiesmhaendel
The document discusses integrating the eagle-i and VIVO ontologies into a single Integrated Semantic Framework (ISF) ontology to represent clinical and research expertise. It describes merging the ontologies by identifying overlapping entities and addressing representation issues like modeling people and their roles over time. The ontology merging process involved referencing existing entities from the source ontologies and incorporating external vocabularies while proposing new design patterns. The goal is to develop a standardized ontology and research profiling data exchange for connecting clinicians and researchers across institutions.
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.
This paper discusses the development and results of SCDA in 2015 at the Text Retrieval Conference (TREC).
My team developed the system in Java utilizing Lucene, MetaMap, and the Stanford Parser. I worked both as a developer and a technical writer with a specific focus on the use of Lucene.
TRG's semantic technology solution can help life sciences companies aggregate and analyze their research and development data to accelerate innovation. It uses linguistic analysis to understand relationships between unstructured data from different sources and systems. This semantic intelligence allows researchers to search for relevant information more precisely and get a holistic view of data, relationships and trends to inform research directions. TRG provides deep domain expertise and claims benefits like automatic categorization and summarization to empower researchers.
The document discusses using WEKA and BioWeka to analyze DNA sequences and perform pattern matching. It summarizes how Eclat filtering and EM clustering are applied to a dataset containing DNA sequences from human and chimpanzee chromosomes. Eclat is used to extract codon frequencies as features, while EM clustering assigns sequences to clusters based on the mixture model with the highest posterior probability. The analysis aims to identify biologically relevant groups of genes and determine chromosomal similarities between humans and chimpanzees.
This document provides release notes for version 1.3.0.0 of the MedITEX IVF and MedITEX Scheduler software. Key updates include:
1) Adding automatic text completion features and incubator protocol importing capabilities.
2) Expanding fields for semen analysis and cryo storage.
3) Displaying attending physician name on home screen and ordering therapies by start date.
4) Allowing treatment protocol selection and saving therapy planning templates.
5) Adding ultrasound examination fields and attaching videos/images.
6) Selecting lab parameters for display and grouping them in the cycle overview.
1) myExperiment is a social software platform that allows scientists to share, reuse, and repurpose workflows in order to reduce time spent on experiments and avoid duplicating work.
2) It has over 950 registered users who have shared over 290 workflows and 100 files across 80 groups. Content on the site sees thousands of downloads and views each month.
3) The platform provides functionality for discovering, executing, and collaborating on workflows. It aims to promote sharing and reuse of workflows across disciplines and experience levels.
Being Reproducible: SSBSS Summer School 2017Carole Goble
Lecture 2:
Being Reproducible: Models, Research Objects and R* Brouhaha
Reproducibility is a R* minefield, depending on whether you are testing for robustness (rerun), defence (repeat), certification (replicate), comparison (reproduce) or transferring between researchers (reuse). Different forms of "R" make different demands on the completeness, depth and portability of research. Sharing is another minefield raising concerns of credit and protection from sharp practices.
In practice the exchange, reuse and reproduction of scientific experiments is dependent on bundling and exchanging the experimental methods, computational codes, data, algorithms, workflows and so on along with the narrative. These "Research Objects" are not fixed, just as research is not “finished”: the codes fork, data is updated, algorithms are revised, workflows break, service updates are released. ResearchObject.org is an effort to systematically support more portable and reproducible research exchange.
In this talk I will explore these issues in more depth using the FAIRDOM Platform and its support for reproducible modelling. The talk will cover initiatives and technical issues, and raise social and cultural challenges.
The document provides an overview of solutions from Quahog Life Sciences including data management, security, analysis, visualization, and applications. The platform allows users to unify multiple data sources, merge them into a single structured data store, and organize data by patients. It uses advanced encryption techniques and offers machine learning capabilities like pattern extraction, segmentation, and predictive models. Visualization features include interactive dashboards with various chart types. Example use cases demonstrate pattern discovery in cancer research and influencer detection in cellular research. Bot applications are described for assisting diabetologists and physicians.
The document provides an overview of solutions from Quahog Life Sciences including data management, security, analysis, visualization, and applications. The platform allows merging of multiple data sources, creation of a logical data model, and organization of patient data. Advanced encryption is used to securely share data. The platform supports machine learning using a recursive neural network and analytics models. Use cases described include pattern discovery in cancer research and influencer detection in cellular research. Visualization capabilities include interactive dashboards with multiple chart types. Additional applications include bot assistance for diabetologists and physicians.
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
The World Wide Web holds a large size of different information. Sometimes while searching the World Wide Web, users always do not gain the type of information they expect. In the subject of information extraction, extracting semantic relationships between terms from documents become a challenge. This paper proposes a system helps in retrieving documents based on the query expansion and tackles the extracting of semantic relationships from biological documents. This system retrieved documents that are relevant to the input terms then it extracts the existence of a relationship. In this system, we use Boolean model and the pattern recognition which helps in determining the relevant documents and determining the place of the relationship in the biological document. The system constructs a term-relation table that accelerates the relation extracting part. The proposed method offers another usage of the system so the researchers can use it to figure out the relationship between two biological terms through the available information in the biological documents. Also for the retrieved documents, the system measures the percentage of the precision and recall.
Knowledge graph applications for cosmetics industryAnton Yuryev
This document summarizes three use cases for Elsevier's deep reading AI and biology knowledge graph. The first use case identifies UV-absorbing compounds for skin care by annotating over 684,000 compounds with relevant cell processes and diseases. The second use case identifies compounds that can modulate estrogen production by analyzing relevant metabolic pathways and regulation networks. The third use case performs transcriptomics analysis of androgenic alopecia to build a regulatory network model and identify new drug targets using differential expression analysis and sub-network enrichment.
Five drug development strategies to combat SARS-CoV2Anton Yuryev
Slides were presented at webinar on “Opportunities & Challenges in Drug Discovery and Development” organised by Elsevier in collaboration with Dr Reddy’s Institute of Life Sciences, Hyderabad on July 16th,2020
Drugs predicted to bind #COVID19 proteins by computational dockingAnton Yuryev
Drugs predicted by computational ligand docking to bind COVID19 proteins from Wu et al 2020, Kandeel et al 2020, Joshi et al 2020, Adem et al 2020 articles
Genetic variations linked to Acute Respiratory Distress syndromeAnton Yuryev
The list of rs Identifiers linked by to ARDS in peer-reviewed scientific literature. This list can help determine individuals at risk to develop severe symptoms from COVID19 infection
AAK1 GAK inhibitors for anti-COVID19 therapyAnton Yuryev
BenevolentAI has reported three AAK1 and GAK kinase inhibitors effective against #coronavirus #COVID19. I publish the list of 35 approved drugs and lead compounds that can inhibit AAK1 and GAK kinases and therefore can be effective against #COVID19. Drugs were found in #Elsevier #PathwayStudio and #Reaxys knowledgebases. To find more drugs that can be effective against #COVID19 please visit #Elsevier Coronavirus Information Center or read my blog about atrategies to find more drugs for #coronavirus
Drug re-positioning for tuberculosis infection in HIV/ADS patients Anton Yuryev
Tuberculosis infection is #1 cause of death among HIV patients in the developing countries. Drugs must not only inhibit tuberculosis but also inhibit HIV infection. Sophisticated searches in #Elsevier #PathwayStudio knowledge graph revealed that several FDA approved drugs have appropriate therapeutic profile.
Patient microbiome analysis using Elsevier text miningAnton Yuryev
This presentation demonstrates how to use Elsevier Text Mining to analyze a patient's microbiome profile from Aperiomics and interpret the results. Multiple Search is used to identify bacterial species from the patient's throat and stool that have been linked to her lung and bowel problems in medical literature. Species linked to her conditions include Streptococcus pneumoniae, Streptococcus pyogenes, Haemophilus parainfluenzae, and Fusobacterium nucleatum in her throat and Bacteroides vulgatus in her stool. Multiple Search also identifies protective species in her stool like Bifidobacterium longum and Faecalibacterium prausnitzii. Antibiotics and probiotics are then suggested
The document analyzes the tumor transcriptomics profile of a patient with stage IV gallbladder cancer. It identifies the top 200 most significantly activated expression regulators in the patient's tumor using sub-network enrichment analysis software. Key regulators identified include histone deacetylases and DNA methyltransferases. The analysis suggests treatment with the HDAC inhibitor vorinostat and discusses how activated regulators like HDACs, PDCD1, and CTLA4 may be contributing to tumor proliferation and immune evasion. Graphical summaries show pathways enriched with active regulators in the tumor related to cancer hallmarks like histone modification and immune response evasion.
OMICs data analysis using Pathway StudioAnton Yuryev
This document provides summaries of 32 publications from 2018-2019 that used Pathway Studio software for OMICs data analysis. The publications covered a wide range of topics including Alzheimer's disease, stress response genes in Drosophila, gene expression following radiotherapy exposure, miRNA and mRNA profiling in horse satellite cells, and proteome changes in plant somatic embryogenesis. Figures from each publication are also presented.
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
This document compares two approaches for identifying potential gene expression regulators from experimental data: Sub-Network Enrichment Analysis (SNEA) in Pathway Studio and Causal Reasoning in Ingenuity Pathway Analysis (IPA). It analyzes a publicly available dataset on Spinal Muscular Atrophy. SNEA identified biological functions and expression regulators more specifically related to neurogenesis, the key process affected in SMA. It produced results with greater relevance to the disease compared to IPA. Mapping genes to a motor neuron differentiation model also showed SNEA results were more consistent with SMN1 knockout.
Analysis of Functional Magnetic Resonance Imaging (fMRI) data from human brai...Anton Yuryev
The presentation from National Institute of Mental Health shows how to use Pathway Studio data for analysis of fMRI from schizophrenia patients. It finds novel proteins that can be used as biomarkers or drug targets for schizophrenia.
Elsevier helps malaria research with comprehensive Plasmodium biology databaseAnton Yuryev
Elsevier professional services constructed the most comprehensive knowledgebase for network and pathway analysis of Plasmodium genome using deep reading NLP technology from thousands scientific publications about Plasmodium parasite. The database allows pathway reconstruction by data-mining human-parasite protein interactions, network enrichment analysis of OMICs data-sets, search for compounds modulating human immune response to parasite or inhibiting Plasmodium proteins. This slide presentation shows some of key features and statistics for this database.
Personalized Medicine: Matching cancer drugs with mechanism (AAPS webinar)Anton Yuryev
Anton Yuryev describes how to identify optimal molecular targets and drugs for personalized cancer treatment using network and pathway analysis of transcriptomics profiles from tumor biopsies. The approach involves determining the most active targets using network analysis, finding cancer hallmark pathways enriched with these targets, and identifying FDA-approved drugs targeting the most active hallmarks. Sub-Network Enrichment Analysis is used to calculate regulator activity from downstream targets in patient profiles. Pathway Studio contains cancer pathway models built from literature to map patient profiles and find druggable targets. The approach is validated for stage IV cancer patients and aims to optimize treatment by targeting multiple identified regulators with drug combinations.
Presentation at Rare Disease conference in San-AntonioAnton Yuryev
Elsevier has significantly reduced the cost of drug development for rare diseases through drug repurposing. They have brought together knowledge about drug targets, effects, and disease biology to identify drugs and nutraceuticals approved by the FDA that could potentially be repositioned to treat rare diseases, eliminating the need for new drug development and clinical trials. Using automated queries of Elsevier knowledgebases, they can provide summaries of potential treatments for a given rare disease, including key researchers and institutions, relevant drug targets, and approved drugs that may be effective - reducing the cost of repositioning existing drugs to under $500,000.
Profiling how Immune inhibitors Secreted by Melanoma affect NK & other immune...Anton Yuryev
The document summarizes how Elsevier's solutions can help identify novel immunotherapy targets for melanoma by integrating data from multiple sources. It describes using natural language processing to extract information on immune mechanisms from over 20,000 articles, identifying 226 proteins secreted by melanoma that inhibit immune activation and 142 that activate immune tolerance. This approach found many potential immunotherapy targets, including examples of novel targets and opportunities for drug repurposing. Integrating these insights could help create models for combinatorial treatments and better match patients to treatments.
Profiling how Immune inhibitors Secreted by Melanoma affect NK & other immune...Anton Yuryev
The document summarizes how Elsevier's solutions can help identify novel immunotherapy targets for melanoma by integrating data from multiple sources. It describes using natural language processing to extract information on immune mechanisms from over 20,000 articles, identifying 226 proteins secreted by melanoma that inhibit immune activation and 142 that activate immune tolerance. This approach found many potential immunotherapy targets, including examples of novel targets and opportunities for drug repurposing. Integrating these insights could help create models for combinatorial treatments and better match patients to treatments.
This document summarizes a presentation by Timothy Hoctor, VP of Professional Services at Elsevier, about Elsevier's strategic vision and professional services. The key points are:
1) Elsevier aims to increase R&D productivity by linking data across the development spectrum and increase return on information through enhanced search and visualization tools.
2) Elsevier's Professional Services team leverages Elsevier's capabilities to provide customized data management and analysis solutions.
3) Elsevier's strategic objective is to become a leading collaborator in R&D data management through services like data mapping, gap analysis, data governance, and integrated data management.
Pathway analysis for personalized oncologyAnton Yuryev
1) The document discusses using pathway analysis and pathway activity signatures to enable more personalized cancer treatment. It outlines calculating major expression regulators from patient omics data and mapping them to cancer pathways to determine activated pathways.
2) Calculating pathway activity signatures which are short allows better patient classification compared to single targets. Pathway activity also allows selection of drugs that inhibit the active pathway.
3) An example shows a patient's tumor signaling pathway was identified and treatment with drugs targeting the pathway led to no cancer metastasis. The approach aims to continue validating with more medical collaborators.
Know the difference between Endodontics and Orthodontics.Gokuldas Hospital
Your smile is beautiful.
Let’s be honest. Maintaining that beautiful smile is not an easy task. It is more than brushing and flossing. Sometimes, you might encounter dental issues that need special dental care. These issues can range anywhere from misalignment of the jaw to pain in the root of teeth.
These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Travel Clinic Cardiff: Health Advice for International TravelersNX Healthcare
Travel Clinic Cardiff offers comprehensive travel health services, including vaccinations, travel advice, and preventive care for international travelers. Our expert team ensures you are well-prepared and protected for your journey, providing personalized consultations tailored to your destination. Conveniently located in Cardiff, we help you travel with confidence and peace of mind. Visit us: www.nxhealthcare.co.uk
5-hydroxytryptamine or 5-HT or Serotonin is a neurotransmitter that serves a range of roles in the human body. It is sometimes referred to as the happy chemical since it promotes overall well-being and happiness.
It is mostly found in the brain, intestines, and blood platelets.
5-HT is utilised to transport messages between nerve cells, is known to be involved in smooth muscle contraction, and adds to overall well-being and pleasure, among other benefits. 5-HT regulates the body's sleep-wake cycles and internal clock by acting as a precursor to melatonin.
It is hypothesised to regulate hunger, emotions, motor, cognitive, and autonomic processes.
Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
1. 1
1
Release Notes, Pathway Studio v12
September 2018
The v12 Release for Pathway Studio finalized the data structure and completed the
support functionalities for Pathway Studio Ontology. The new Pathway Studio offers
Cloud-hosted solutions for all workflows with improved content accessibility,
advanced search capabilities and results sharing. New, highly relevant content was
also added. The coverage of diseases, chemical and cellular effects in the Mammal
and MammalPlus Knowledgebases were augmented with organ and tissue data. A
new entity was added covering genetic variants extracted from the literature and
imported from ClinVar with relations to genes and function. The Plant
Knowledgebase was expanded with agriculture-relevant plant species. Finally,
relevant drug-target relations from Reaxys Medicinal Chemistry are now integrated
into and mapped to concepts in Pathway Studio.
Five-Point Summary
1. Change font size and entity size for each individual entity: Modify the
look of pathways to fit your presentation and publication needs.
2. Improved and expanded support of ontologies:
a. Explore and search terms in Pathway Studio Ontology in a new
Ontology Browser
b. Right-click an entity and select Show in Ontology to look it up in the
context of ontologies
c. Use ontologies to add entities to pathways (e.g., add all inflammatory
diseases)
d. Use ontologies in experiment analysis (e.g., find all kinases).
3. Export improvements: Export entity properties (such as entity name and
IDs) together with relation properties directly from the Relation Table View.
4. Share pathways and experiments in User Groups (for PSE only):
Admins can set up User Groups for collaborating teams. Within a User
Group you can:
a. Copy a pathway, experiment or group to the Group Projects and allow
other group members to modify your file.
b. Copy a shortcut to your pathway, experiment or group to the Group
Projects so other group members can see but not modify your file.
5. Content Enhancement:
a. Enhanced anatomy data for insights at organ and tissue level
b. Integration of drug-target relations from Reaxys Medicinal Chemistry
c. A new entity centered on SNV/SNP genetic variation
d. Expanded species coverage of the Plant Knowledgebase
2. 2
New Search Capabilities for Fast and Unparalleled Results
Building on the new curated pathway data organization and structure introduced in v11.4,
the v12 Release for Pathway Studio allows you to browse and view the context of specific
entities within the hierarchical tree of the Pathway Studio Ontology. Thus, you can interpret
search results within the context of the entity’s ontology to answer questions about the
relevance of retrieved hits. You can also increase specificity of your search by quickly
determining ontological terms that will produced the right results.
Pathway Studio supports two ontologies: Gene Ontology (GO) for protein and gene names,
and Pathway Studio Ontology, which supports all entities in the Knowledgebases — cell
processes, clinical parameters, complexes, diseases, functional classes, proteins and gene
names, small molecules and treatments. View an ontology by right-clicking the highlighted
entity and selecting the GO or Pathway Studio Ontology.
You can begin a focused search by first browsing the Pathway Studio Ontology (Fig. 1) to
find terms aligned with your query. Alternatively, a broad search will return results from all
ontology terms associated with the query, but with the update you can right-click any entity in
the results table to view its location in the ontology and determine if the information is
relevant to your query. The Pathway Studio Ontology is an underlying structure of Pathway
Studio, so you can access it on entities in curated or user-defined pathways (Fig. 2), as well
as on entities aligned to experimental results (Fig. 3) curated with Pathway Studio
relationships.
Fig. 1. Browse Pathway Studio Ontology to select objects and concepts of relevance to your search.
3. 3
Fig. 2. Use Pathway Studio Ontology to build pathways. Select Specific Ontology Categories in Network Builder
and pick entities of interest.
Fig. 3. Select the Ontological Categories tab to use ontologies in experiment analyses.
4. 4
Facilitated export of entity and relation properties
An upgrade to the export functionality of Pathway Studio allows selecting entities and
relation properties to be exported directly from the Relation Table View. Click Export and
select Excel CSV/tab-delimited from the dropdown menu (Fig. 4). As output, you receive a
single table with all selected properties.
Fig. 4. Improved export functionality allows you to select entity and relation properties from the Relation Table
View and export them together into a single table.
5. 5
Sharing Results in User Groups (available only in PSE)
In response to customer feedback, Pathway Studio Web and Enterprise Cloud now enable
creating User Groups to share results and projects with colleagues. Create one or more
User Groups with different members and they appear listed under My Projects in Pathway
Studio (Fig. 5). Shared results and pathways/networks are editable projects, which
colleagues open and save. After editing, they can share the edits with the group.
Fig. 5. Different paste options allow sharing pathways, results and experiments with colleagues in a User Group.
Streamlined and improved Admin Functions (available only in PSE)
Users with Admin rights can now manage Users and set up User Groups under the Admin
menu. Furthermore, users with a Publisher role can easily submit pathways for approval and
publication as user-generated pathways. Submitted pathways appear first in the
Submissions tab visible to users with an Approver role and, once approved, become visible
to everyone at the institution. The Submissions tab also accommodates approval and
publication of experiments and User Groups.
Content Enhancements
Enhanced Anatomy data for Insights at Organ and Tissue Level
The addition of 3,682 organs and tissues to the Pathway Studio Mammal and MammalPlus
KnowledgeBases augments the number of relations that can be probed in Pathway Studio to
>1.7 million. Tap into regulatory effects, gene expression, state changes and functional
associations connecting cell processes, clinical parameters, diseases, compounds, and
proteins to organs and tissues.
6. 6
Integration of Drug-Target Relations from Reaxys Medicinal Chemistry
Reaxys Medicinal Chemistry (RMC) is a market-leading research system for bioactivity data.
With over 20 million drug-target relations, RMC supports early drug discovery. Relevant
relations (for human, mouse and rat) are now integrated and mapped to concepts in
Pathway Studio, expanding the small molecules dataset to over 940,000 entities and
delivering >1.1 million annotated relations for human, >32,000 for mouse and >140,000 for
rat.
Genetic Variant: a New SNV/SNP-Centered Entity
A new database focusing on single-nucleotide variants (SNV) or single-nucleotide
polymorphisms (SNP) adds another entity type to Pathway Studio and another dimension to
explore the impact of genetic modifications on pathways and their functions. Data are
extracted by MedScan from the scientific literature as well as imported from ClinVar, and
have resulted to date in >330,000 entities, >130,000 relations by functional association
(disease, clinical parameter, biological process), and >320,000 relations to genes.
Expanded Plant Knowledgebase for Agricultural Biology Research
To improve content for basic and applied agricultural research, the Pathway Studio Plant
Knowledgebase has been expanded with seven model organisms. The models support
research into gene functionality of the model and similar organisms. Select a specific model
organism under Preferences.
Organism Common name Description
Brachypodium
distachyon
Stiff brome
Closely related to and a model organism for
wheat and barley.
Populus trichocarpa California poplar A model organism for forestry research.
Vitis vinifera Common grape
A model organism for agricultural and
viticultural research.
Glycine max Soybean A model organism for crop research.
Sorghum bicolor Sorghum A model organism for crop research.
Solanum lycopersicum Tomato
A model organism for crop and agricultural
research.
Fragaria vesca
Woodland or
wild strawberry
A model for plants exhibiting multiploidy.