A presentation of the bioschemas implementation in the EMBL-EBI Biosamples database presented at the Bioschemas adoption meeting on October 2nd 2017. Bioschemas is a proposed extension of Schema.org.
This document describes research on transforming a Wiki system into a relational database by adding search extension capabilities. As a proof of concept, the researchers created a database of 6902 flavonoid molecular structures from over 1687 plant species implemented on the MediaWiki platform. The system allows users to freely enter information while also enabling structured text searches, realizing relational database operations. This approach benefits from both the flexible Wiki style and query abilities of relational databases.
Philippe Rocca-Serra discusses streamlining the deposition of targeted metabolomics datasets to public repositories. He describes collaborating with Biocrates and EMBL-EBI Metabolights to develop a deposition pipeline. The pipeline involves exporting XML from Biocrates software, uploading to EMBL-EBI via Aspera, converting to ISA-Tab and MAF formats, and minting an accession number. Three datasets involving thousands of human plasma and urine samples are being handled through this pipeline. The take home message is that data custodians and suppliers can work together efficiently to avoid data loss and ensure visibility of datasets.
SEEK is an open-source platform for scientists to store, share, and collaborate on heterogeneous data, models, and standard operating procedures. It was developed by researchers in the UK and Germany to facilitate data sharing across multi-group projects. SEEK allows scientists to organize experiments and data using ISA-TAB standards, interlink related assets, and control access to assets at various stages of research from private to public. Key features include hosting and simulating SBML models, exploring and annotating spreadsheets, and finding expertise and collaborators through people profiles.
BioDBCore: Current Status and Next DevelopmentsPascale Gaudet
The document discusses BioDBCore, a collaborative project aimed at gathering and standardizing metadata about biological databases. It provides an overview of BioDBCore's goals of improving data integration, encouraging standards, and maximizing resources. BioDBCore is led by Pascale Gaudet and Philippe Rocca-Serra and implemented on the BioSharing website. The document outlines the BioDBCore descriptors for databases and provides an example entry for the dictyBase database. It discusses maintaining and expanding BioDBCore records with the help of database providers and journals.
This document discusses making biobank data and samples FAIR (Findable, Accessible, Interoperable, and Reusable).
It explains the four FAIR principles and provides examples of how to apply each one. To make resources findable, they need unique and persistent identifiers, rich metadata, and to be discoverable through other systems. To make them accessible, they need to be retrievable using open standards. To make them interoperable, standards for knowledge representation like ontologies should be used. And to make them reusable, they need to be richly described and released with clear usage terms and provenance.
The document recommends three steps to make samples and data FAIR: include sufficient metadata using
David Van Enckevort - FAIR sample and data access DataSciSIG
David van Enckevort from the University of Groningen describes FAIR Sample and Data Access in Biobanking and Biorepositories.
This talk was sponsored by the NIH Data Science Special Interest Group and part of a webinar panel on June 23, 2017 on Global Biobanking and Access to Specimens.
The Diversity of Biomedical Data, Databases and Standards (Research Data Alli...Peter McQuilton
A 10 minute presentation given in Denver (CO) on the 15th September as part of the IG Elixir Bridging Force, WG Biosharing Registry,WG Data Type Registries,WG Metadata Standards Catalog joint session of the Research Data Alliance 8th Plenary (part of International Data Week).
This presentation covers the proliferation of data, databases, and data standards in biomedicine, and how BioSharing can help inform and educate users on this landscape and relationships between data, databases and data standards.
OpenAIRE Guidelines for data providers: new Metadata Application Profile for ...OpenAIRE
Presentation at the "OpenAIRE webinar series for repository managers 2017/2018" - Nov. 14, 2017 (11h00 CET) | "OpenAIRE Guidelines for data providers: new Metadata Application Profile for Literature Repositories", presented by Jochen Schirrwagen, Univ. Bielefeld.
This document describes research on transforming a Wiki system into a relational database by adding search extension capabilities. As a proof of concept, the researchers created a database of 6902 flavonoid molecular structures from over 1687 plant species implemented on the MediaWiki platform. The system allows users to freely enter information while also enabling structured text searches, realizing relational database operations. This approach benefits from both the flexible Wiki style and query abilities of relational databases.
Philippe Rocca-Serra discusses streamlining the deposition of targeted metabolomics datasets to public repositories. He describes collaborating with Biocrates and EMBL-EBI Metabolights to develop a deposition pipeline. The pipeline involves exporting XML from Biocrates software, uploading to EMBL-EBI via Aspera, converting to ISA-Tab and MAF formats, and minting an accession number. Three datasets involving thousands of human plasma and urine samples are being handled through this pipeline. The take home message is that data custodians and suppliers can work together efficiently to avoid data loss and ensure visibility of datasets.
SEEK is an open-source platform for scientists to store, share, and collaborate on heterogeneous data, models, and standard operating procedures. It was developed by researchers in the UK and Germany to facilitate data sharing across multi-group projects. SEEK allows scientists to organize experiments and data using ISA-TAB standards, interlink related assets, and control access to assets at various stages of research from private to public. Key features include hosting and simulating SBML models, exploring and annotating spreadsheets, and finding expertise and collaborators through people profiles.
BioDBCore: Current Status and Next DevelopmentsPascale Gaudet
The document discusses BioDBCore, a collaborative project aimed at gathering and standardizing metadata about biological databases. It provides an overview of BioDBCore's goals of improving data integration, encouraging standards, and maximizing resources. BioDBCore is led by Pascale Gaudet and Philippe Rocca-Serra and implemented on the BioSharing website. The document outlines the BioDBCore descriptors for databases and provides an example entry for the dictyBase database. It discusses maintaining and expanding BioDBCore records with the help of database providers and journals.
This document discusses making biobank data and samples FAIR (Findable, Accessible, Interoperable, and Reusable).
It explains the four FAIR principles and provides examples of how to apply each one. To make resources findable, they need unique and persistent identifiers, rich metadata, and to be discoverable through other systems. To make them accessible, they need to be retrievable using open standards. To make them interoperable, standards for knowledge representation like ontologies should be used. And to make them reusable, they need to be richly described and released with clear usage terms and provenance.
The document recommends three steps to make samples and data FAIR: include sufficient metadata using
David Van Enckevort - FAIR sample and data access DataSciSIG
David van Enckevort from the University of Groningen describes FAIR Sample and Data Access in Biobanking and Biorepositories.
This talk was sponsored by the NIH Data Science Special Interest Group and part of a webinar panel on June 23, 2017 on Global Biobanking and Access to Specimens.
The Diversity of Biomedical Data, Databases and Standards (Research Data Alli...Peter McQuilton
A 10 minute presentation given in Denver (CO) on the 15th September as part of the IG Elixir Bridging Force, WG Biosharing Registry,WG Data Type Registries,WG Metadata Standards Catalog joint session of the Research Data Alliance 8th Plenary (part of International Data Week).
This presentation covers the proliferation of data, databases, and data standards in biomedicine, and how BioSharing can help inform and educate users on this landscape and relationships between data, databases and data standards.
OpenAIRE Guidelines for data providers: new Metadata Application Profile for ...OpenAIRE
Presentation at the "OpenAIRE webinar series for repository managers 2017/2018" - Nov. 14, 2017 (11h00 CET) | "OpenAIRE Guidelines for data providers: new Metadata Application Profile for Literature Repositories", presented by Jochen Schirrwagen, Univ. Bielefeld.
CEDAR is a metadata management tool that lets user define metadata templates using a well described yet flexible metdata format. CEDAR then presents the forms represented by those templates to other users to fill out. CEDAR offers semantic precision (with support from the BioPortal ontology repository), metadata completion assistance, intelligent recommendations, support for JSON-LD and RDF metadata export, and an easy-to-use user interface.
The document discusses data standards that have been developed for systems biology. Standards facilitate sharing experimental data, allow open-source software development, and are increasingly required for journal submissions. Standards are generally developed organically by academic groups when experimental techniques become established. Examples of standards discussed include PRIDE XML for proteomics data, MeMo for metabolomics data, SABIO-RK for enzyme kinetics data, and SBML for modeling biological pathways and running simulations. SBML has enabled the development of over 200 simulation tools and resources like Biomodels.net for sharing models.
The document discusses data standards that have been developed for systems biology. Standards facilitate sharing experimental data, allow open-source software development, and are increasingly required for journal submissions. Standards are generally developed by academic groups when an experimental technique becomes established. Examples of standards discussed include mzData for proteomics data, MeMo for metabolomics data, SBML for biological models, and SED-ML/SBRML for simulation experiments and results. Overall, data standards greatly aid computational systems biology by providing frameworks for data sharing and software development.
Green Shoots:Research Data Management Pilot at Imperial College LondonTorsten Reimer
The document summarizes the results of a research data management (RDM) pilot project at Imperial College London. It describes how £100k in funding was provided for six academic projects to develop exemplars of best practices in RDM. The funded projects developed various tools and frameworks to improve data curation, sharing, and citation. Overall, the pilot demonstrated that innovative RDM is possible but also difficult and expensive to develop sustainably. It helped establish an initial RDM community at Imperial.
Guiding through a typical Machine Learning PipelineMichael Gerke
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
Structural Bioinformatics - Homology modeling & its ScopeNixon Mendez
Homology modeling also known as comparative modeling uses homologous sequences with known 3D structures for the modelling and prediction of the structure of a target sequence
Homology modeling is one of the most best performing prediction methods that gives “accurate” predicted models.
ChemSpider – disseminating data and enabling an abundance of chemistry platformsKen Karapetyan
ChemSpider is one of the chemistry community’s primary public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data to many tens of websites and software applications at this point. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of solutions that it helps to enable. We will also discuss some of the future directions for the project that are envisaged and how we intend to continue expanding the impact for the platform.
Tracking progress through the laboratory pipeline, keeping all required products together, consistent data assessment, analysis-lab feedback loop, key elements of a data management database (LIMS)
Preparing your data for sharing and publishingVarsha Khodiyar
This document provides information on preparing data for sharing and publishing. It discusses organizing data through clear file and folder labeling, including additional context about methods and instruments. It also describes publishing data through journals like Scientific Data, which provide peer review and credit. Sensitive data requires careful handling and may be suitable for controlled access repositories. Overall the document offers guidance on effective data organization, documentation, sharing and receiving credit for shared data.
Leveraging Oracle's Life Sciences Data Hub to Enable Dynamic Cross-Study Anal...Perficient
This document discusses leveraging Oracle's Life Sciences Data Hub to enable dynamic cross-study analysis. It provides an overview of dynamic analytics and a systematic four-stage approach: 1) data preparation, 2) data selection and exploration, 3) model building and analytics, and 4) deployment and reuse. Key aspects of each stage are described, including conforming data, interactively subsetting data, selecting and building analytical models, and creating reusable analysis components. The proposed environment incorporates the Oracle Life Sciences Data Hub, SAS, and other tools. BioPharm Services are also briefly described to support integration and analytics.
The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developedthe CEDAR Workbenchis a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, REST-based environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.
This document discusses Bioschemas.org, a community initiative to develop schemas based on Schema.org to improve findability and accessibility of life science data. It aims to standardize metadata for datasets, repositories, and other bio entities. Bioschemas.org adapts Schema.org and specifies restrictions, constraints, and extensions. It held meetings in October 2017 to socialize initial draft specifications and expand contributor community including EMBL-EBI and other supporters.
This document discusses metadata, including what it is, why it is important, common components of metadata records, examples of metadata standards, and tips for writing good metadata. Metadata captures key details about data, such as who created it, when, how, and why, to facilitate discovering, understanding, and reusing the data. Standards provide consistency for computer interpretation and searching. Good metadata includes specific, accurate, and complete details to fully document data.
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...Peter McQuilton
This document summarizes the work of the FAIRsharing project, which maps databases, standards, and policies to assess and improve their FAIRness. FAIRsharing aims to increase guidance for users and visibility for producers of these resources. It provides a registry of over 500 curated records describing digital assets. FAIRsharing also works to enable the FAIR principles by ensuring these resources are findable, accessible, interoperable, and reusable. Users are encouraged to claim records, provide feedback, and formally cite resources to support these goals.
BlueBrain Nexus Technical IntroductionBogdan Roman
BlueBrain Nexus is a data management platform that enables modeling of data from different domains according to FAIR principles. It uses semantic web technologies like JSON-LD and SHACL to describe, constrain, relate, and evolve data models over time. Nexus treats provenance as a first class citizen and provides semantic search, publishing, and integration capabilities for domain agnostic and interoperable data management.
Part 2 of the MOspace training session, offered at University of Missouri, Saint Louis, on February 9, 2011. This portion of the training offers a more in-depth look at how to work with MOspace.
This document summarizes the work of the GA4GH Metadata Task Team (MTT). The MTT aims to address challenges with metadata standards and use cases across task teams. Initial projects include ArrayMap, Beacon+, and BioSamples. The MTT has integrated DIPG cancer genome data into these resources to enable querying variants associated with specific phenotypes. Issues with real-world metadata complexity are addressed through semantic services and mappings between standards. The long-term vision involves leveraging clinical data through the sample entity.
Ontologies for life sciences: examples from the gene ontologyMelanie Courtot
Ontologies for life sciences: examples from the Gene Ontology
The document discusses ontologies for life sciences, using the Gene Ontology (GO) as an example. It provides an overview of GO, describing it as a way to capture biological knowledge for gene products in a written and computable form using a set of concepts and relationships arranged hierarchically. GO allows consistent descriptions of genes/gene products across databases. Model organism databases provide annotations connecting genes to GO terms. The GO is a collaborative effort to address the need for consistent descriptions of genes.
CEDAR is a metadata management tool that lets user define metadata templates using a well described yet flexible metdata format. CEDAR then presents the forms represented by those templates to other users to fill out. CEDAR offers semantic precision (with support from the BioPortal ontology repository), metadata completion assistance, intelligent recommendations, support for JSON-LD and RDF metadata export, and an easy-to-use user interface.
The document discusses data standards that have been developed for systems biology. Standards facilitate sharing experimental data, allow open-source software development, and are increasingly required for journal submissions. Standards are generally developed organically by academic groups when experimental techniques become established. Examples of standards discussed include PRIDE XML for proteomics data, MeMo for metabolomics data, SABIO-RK for enzyme kinetics data, and SBML for modeling biological pathways and running simulations. SBML has enabled the development of over 200 simulation tools and resources like Biomodels.net for sharing models.
The document discusses data standards that have been developed for systems biology. Standards facilitate sharing experimental data, allow open-source software development, and are increasingly required for journal submissions. Standards are generally developed by academic groups when an experimental technique becomes established. Examples of standards discussed include mzData for proteomics data, MeMo for metabolomics data, SBML for biological models, and SED-ML/SBRML for simulation experiments and results. Overall, data standards greatly aid computational systems biology by providing frameworks for data sharing and software development.
Green Shoots:Research Data Management Pilot at Imperial College LondonTorsten Reimer
The document summarizes the results of a research data management (RDM) pilot project at Imperial College London. It describes how £100k in funding was provided for six academic projects to develop exemplars of best practices in RDM. The funded projects developed various tools and frameworks to improve data curation, sharing, and citation. Overall, the pilot demonstrated that innovative RDM is possible but also difficult and expensive to develop sustainably. It helped establish an initial RDM community at Imperial.
Guiding through a typical Machine Learning PipelineMichael Gerke
Many People are talking about AI and Machine Learning. Here's a quick guideline how to manage ML Projects and what to consider in order to implement machine learning use cases.
Structural Bioinformatics - Homology modeling & its ScopeNixon Mendez
Homology modeling also known as comparative modeling uses homologous sequences with known 3D structures for the modelling and prediction of the structure of a target sequence
Homology modeling is one of the most best performing prediction methods that gives “accurate” predicted models.
ChemSpider – disseminating data and enabling an abundance of chemistry platformsKen Karapetyan
ChemSpider is one of the chemistry community’s primary public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data to many tens of websites and software applications at this point. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of solutions that it helps to enable. We will also discuss some of the future directions for the project that are envisaged and how we intend to continue expanding the impact for the platform.
Tracking progress through the laboratory pipeline, keeping all required products together, consistent data assessment, analysis-lab feedback loop, key elements of a data management database (LIMS)
Preparing your data for sharing and publishingVarsha Khodiyar
This document provides information on preparing data for sharing and publishing. It discusses organizing data through clear file and folder labeling, including additional context about methods and instruments. It also describes publishing data through journals like Scientific Data, which provide peer review and credit. Sensitive data requires careful handling and may be suitable for controlled access repositories. Overall the document offers guidance on effective data organization, documentation, sharing and receiving credit for shared data.
Leveraging Oracle's Life Sciences Data Hub to Enable Dynamic Cross-Study Anal...Perficient
This document discusses leveraging Oracle's Life Sciences Data Hub to enable dynamic cross-study analysis. It provides an overview of dynamic analytics and a systematic four-stage approach: 1) data preparation, 2) data selection and exploration, 3) model building and analytics, and 4) deployment and reuse. Key aspects of each stage are described, including conforming data, interactively subsetting data, selecting and building analytical models, and creating reusable analysis components. The proposed environment incorporates the Oracle Life Sciences Data Hub, SAS, and other tools. BioPharm Services are also briefly described to support integration and analytics.
The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developedthe CEDAR Workbenchis a suite of Web-based tools and REST APIs that allows users to construct metadata templates, to fill in templates to generate high-quality metadata, and to share and manage these resources. The CEDAR Workbench provides a versatile, REST-based environment for authoring metadata that are enriched with terms from ontologies. The metadata are available as JSON, JSON-LD, or RDF for easy integration in scientific applications and reusability on the Web. Users can leverage our APIs for validating and submitting metadata to external repositories. The CEDAR Workbench is freely available and open-source.
This document discusses Bioschemas.org, a community initiative to develop schemas based on Schema.org to improve findability and accessibility of life science data. It aims to standardize metadata for datasets, repositories, and other bio entities. Bioschemas.org adapts Schema.org and specifies restrictions, constraints, and extensions. It held meetings in October 2017 to socialize initial draft specifications and expand contributor community including EMBL-EBI and other supporters.
This document discusses metadata, including what it is, why it is important, common components of metadata records, examples of metadata standards, and tips for writing good metadata. Metadata captures key details about data, such as who created it, when, how, and why, to facilitate discovering, understanding, and reusing the data. Standards provide consistency for computer interpretation and searching. Good metadata includes specific, accurate, and complete details to fully document data.
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...Peter McQuilton
This document summarizes the work of the FAIRsharing project, which maps databases, standards, and policies to assess and improve their FAIRness. FAIRsharing aims to increase guidance for users and visibility for producers of these resources. It provides a registry of over 500 curated records describing digital assets. FAIRsharing also works to enable the FAIR principles by ensuring these resources are findable, accessible, interoperable, and reusable. Users are encouraged to claim records, provide feedback, and formally cite resources to support these goals.
BlueBrain Nexus Technical IntroductionBogdan Roman
BlueBrain Nexus is a data management platform that enables modeling of data from different domains according to FAIR principles. It uses semantic web technologies like JSON-LD and SHACL to describe, constrain, relate, and evolve data models over time. Nexus treats provenance as a first class citizen and provides semantic search, publishing, and integration capabilities for domain agnostic and interoperable data management.
Part 2 of the MOspace training session, offered at University of Missouri, Saint Louis, on February 9, 2011. This portion of the training offers a more in-depth look at how to work with MOspace.
This document summarizes the work of the GA4GH Metadata Task Team (MTT). The MTT aims to address challenges with metadata standards and use cases across task teams. Initial projects include ArrayMap, Beacon+, and BioSamples. The MTT has integrated DIPG cancer genome data into these resources to enable querying variants associated with specific phenotypes. Issues with real-world metadata complexity are addressed through semantic services and mappings between standards. The long-term vision involves leveraging clinical data through the sample entity.
Ontologies for life sciences: examples from the gene ontologyMelanie Courtot
Ontologies for life sciences: examples from the Gene Ontology
The document discusses ontologies for life sciences, using the Gene Ontology (GO) as an example. It provides an overview of GO, describing it as a way to capture biological knowledge for gene products in a written and computable form using a set of concepts and relationships arranged hierarchically. GO allows consistent descriptions of genes/gene products across databases. Model organism databases provide annotations connecting genes to GO terms. The GO is a collaborative effort to address the need for consistent descriptions of genes.
The Gene Ontology & Gene Ontology Annotation resourcesMelanie Courtot
The Gene Ontology (GO) provides structured controlled vocabularies for describing gene and gene product attributes across species. It includes three ontologies for molecular function, biological process, and cellular component. The GO is manually developed and electronically annotated to gene products to capture biological knowledge in a computable form. The GO Consortium aims to develop and maintain the GO through manual and computational methods, and to provide public GO annotation data and tools.
Standards for public health genomic epidemiology - Biocuration 2015Melanie Courtot
A presentation introducing genomic epidemiology and its application in public health. It also explains the need for standards to support the Canadian Integrated Rapid Infectious Disease Analysis platform which implements genomic epidemiology analyses for detection and investigation of infectious disease outbreaks caused by food-borne pathogens.
This document provides an overview of big data, the semantic web, ontologies, and the IRIDA platform. It discusses how big data is large and complex data, standards for the semantic web like URIs, RDF, SPARQL and OWL, using ontologies for definitions and reasoning, examples of existing ontologies, and how the IRIDA platform adds semantic web standards. The overview aims to provide background on these topics for analysis and integration of large and complex data sets.
A presentation supporting discussion on (1) how could MedDRA benefit from an ontological representation and (2) how we can practically move forward in creating this formalization.
Presented at the International Conference on Biomedical Ontology 2014 in Houston, TX: http://icbo14.com/sessions/meddra-and-ontology/
This document discusses using an ontology-based approach to automatically classify adverse event reports at a similar accuracy as manual classification. It tested classifying over 6000 vaccine adverse event reports for anaphylaxis using terms from an adverse event reporting ontology mapped to guidelines. The automated approach achieved a maximum sensitivity of 57% and specificity of 97%. Additional techniques improved sensitivity to 92% while maintaining high specificity. The results demonstrate the potential for ontologies to help analyze large datasets of adverse event reports.
Diagnostic criteria and clinical guidelines standardization to automate case ...Melanie Courtot
This document discusses standardizing clinical guidelines for adverse event classification using the Adverse Event Reporting Ontology (AERO). It describes how AERO encodes guidelines like Brighton Collaboration for diagnosing conditions like anaphylaxis from vaccine reports. It also discusses how AERO can integrate data like classifying reports in the Vaccine Adverse Event Reporting System according to encoded guidelines and linking to other datasets like DrugBank. The goal is automated adverse event diagnosis and integration of reporting data.
This document discusses standardizing adverse event reporting following immunization to improve timely and cost-effective signal detection of potential safety issues. It proposes translating existing case definitions for adverse events into a computer-readable format called the Adverse Event Reporting Ontology (AERO). This ontology would then be applied to current reporting system data to automatically classify cases and detect safety signals faster and at lower cost than current manual review methods. The goals are to increase consistency in reporting and allow easier querying of reports to help regulators identify potential safety issues.
Adverse Events Following Immunization: Reporting standardization, Automatic C...Melanie Courtot
Analysis of spontaneous reports of Adverse Events Following Immunization (AEFIs) is an important way to identify potential problems in vaccine safety and efficacy and summarize experience for dissemination to health care authorities. The Adverse Event Reporting Ontology (AERO) we are building plays a role in increasing accuracy and quality of reporting, ultimately enhancing response time to adverse event signals.
BUILDING THE OBO FOUNDRY – ONE POLICY AT A TIMEMelanie Courtot
Policy drafting,discussion and implementation is not the most exciting or interesting thing to do when developing new resources. However, when trying to identify existing work that can be built upon in one’s project, such policies are critical to allow interoperability and reliability. We describe some tools and guidelines developed under the OBO Foundry umbrella, and show how they help realize crit- ical maintenance functions, increasing overall quality and sustainability of resources.
The document summarizes a presentation on developing an Adverse Event Reporting Ontology (AERO) to standardize adverse event reporting. It discusses the need for standardized adverse event reporting, describes how AERO represents adverse event guidelines and enables more complex queries. It proposes a three step approach: 1) use standard definitions, 2) convert guidelines to a computer format, 3) implement in reporting systems to support clinicians during reporting.
This document discusses developing an ontology for standardizing adverse event reporting. It proposes using existing case definitions, like those from the Brighton Collaboration, to create a computer-readable ontology. This would enable unambiguous reporting of adverse events following immunization. It would also allow complex queries of reported data and help confirm diagnostic determinations. The ontology could be implemented in reporting systems to improve data quality and interoperability.
The document discusses MIREOT (Minimal information to reference external ontology terms), an approach used by the Ontology for Biomedical Investigations (OBI) project to import terms from external ontologies. It describes three approaches to importing terms - creating duplicate terms, importing modules, and full imports. It proposes importing only the classes needed using a minimal set of information to unambiguously identify terms from external ontologies. This process has been implemented in OBI and an online tool called OntoFox has been developed to facilitate the MIREOT process.
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
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Adhd Medication Shortage Uk - trinexpharmacy.comreignlana06
The UK is currently facing a Adhd Medication Shortage Uk, which has left many patients and their families grappling with uncertainty and frustration. ADHD, or Attention Deficit Hyperactivity Disorder, is a chronic condition that requires consistent medication to manage effectively. This shortage has highlighted the critical role these medications play in the daily lives of those affected by ADHD. Contact : +1 (747) 209 – 3649 E-mail : sales@trinexpharmacy.com
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
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!
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
5. Bioschemas Samples: milestones
• M1: Analysis and mapping of metadata already used in
existing sample registries and defined by existing standards
e.g. MIABIS
• M2: Define minimum information guidelines based on the
results of the mapping and feedback from registries
• Identify minimum set of properties
• M3: Test adoption and improve specification with selected
data repositories
• M4: Propose any new suggested types or properties to
schema.org
6. Bioschemas Samples: deliverables
• D1: Bioschemas specification
• D2: Data repository using Bioschemas compliant markup
• D3: Data registry using Bioschemas compliant markup