NCATS Biomedical Data Translator prototypes registry and documentation.
See the official Translator prototypes registry at https://ncatstranslator.github.io
Services to explore and validate implementations against the Translator standards.
Standard recommended when serving an API in the Translator project. It consists in a JSON model for graph data, and allow to query and get answer from Translator APIs.
KGX (Knowledge Graph Exchange) is a Python library and set of command line utilities for exchanging Knowledge Graphs (KGs) that conform to or are aligned to the Biolink Model.
- See KGX presentation.
- See KGX data preparation documentation.
- See the GitHub repository of the Knowledge Graph Exchange Working Group
- Open API: https://nodenormalization-sri.renci.org/apidocs/
- NodeNormalization Jupyter Notebook for documentation
- Open API: https://edgenormalization-sri.renci.org/apidocs/
- EdgeNormnalization Jupyter Notebook for documentation
- Open API: https://monarch-sandbox.cgrb.oregonstate.edu/docs
- BLCompliance Jupyter Notebook for documentation
- Columbia Open Health Data (COHD)
- Clinical associations mined from observational EHR data
- Conditions, drugs, procedures, gender, race, ethnicity
- EHR prevalence and Co-occurrence count
- Associations calculated from EHR prevalence and co-occurrence count
- Privacy protection measures
- A framework and Command Line Interface for building and deploying Translator data and services in a reproducible manner.
- Documentation and tools to transform your data to a BioLink-compliant RDF knowledge graph
- Automatically deploy a Reasoner API over a BioLink-compliant RDF triplestore
- Deploy additional interfaces to explore the knowledge graph
- COHD Reasoner API at http://cohd.io/api
- Data2Services documentation at https://d2s.semanticscience.org
- Data2Services Reasoner API over BioLink RDF at http://api.trek.semanticscience.org (see on GitHub)
- Into-the-graph web UI to browse a BioLink RDF triplestore leveraging metadata and services at http://trek.semanticscience.org
- GitHub repositories for Data2Services project template and command line interface.
Docker must be installed.
A Molecular Data Provider translating molecular scale to systems scale through a Reasoner API.
- Open API: https://translator.broadinstitute.org/molecular_data_provider/api
- Reasoner API: https://smart-api.info/ui/912372f46127b79fb387cd2397203709#
A tool to curate genetic associations for complex diseases, interpret their biological effects, and make these data available to the Translator.
- Patient data + environmental exposures data
- Integrated at patient- and visit-level
- UNC Health Care System (UNCHCS) + NIEHS Environmental Polymorphisms Registry (EPR)
- Observational EHR data, EPR survey data, SNP data, exposures data
- Available for years 2010 – 2016
- ICEES+ Open APIs
Up-to-date, BioLink-compatible, knowledge graph composed of assertions mined from the available full-text biomedical literature using high-performance text mining systems
- Access Heterogeneous Data
- Researcher clinical data
- Knowledge captured in the Biomedical Data Translator project
- Automate Source Selection
- Effective Question-Response Ranking
- Actionable Information
Big GIM (Gene Interaction Miner), function interaction data for all pairs of genes. Functional interaction data are available from four different sources: 1) tissue-specific gene expression correlations from healthy tissue samples (GTEx), 2) tissue-specific gene expression correlations from cancer samples (TCGA), 3) tissue-specific probabilities of function interaction (GIANT), and 4) direct interactions (BioGRID).
Big CLAM (Cell Line Association Miner), integrates large-scale high-quality data of various cell line resources to uncover associations between genomic and molecular features of cell lines, drug response measurements and gene knockdown viability scores. The cell line data comes from five different sources: 1) CCLE - Cancer Cell Line Encyclopedia, 2) GDSC - Genomics of Drug Sensitivity in Cancer, 3) CTRP - Cancer Therapeutics Response Portal, 4) CMap - Connectivity Map, and 5) CDM - Cancer Dependency Map.
- Reasoner API: http://biggim.ncats.io/api
- Running instructions
- Big GIM II API: https://github.com/gloriachin/BigGIMII_API
Documentation and integration to
d2s started here.
Build and deploy BioThings APIs from flat data files.
- API-fy knowledge sources on demand
- Use BioThings SDK in Python to download and parse input data sources
- Integrate your API to a meta-KG using Smart API
Translator KP APIs powered by BioThings SDK: https://biothings.ncats.io
- GitHub: https://github.com/SmartAPI/smartAPI
- Up-to-date Meta-KG: https://smart-api.info/registry/translator/meta-kg
Example Disease KP API: https://biothings.ncats.io/DISEASES
Up-to-date Meta-KG: https://smart-api.info/registry/translator/meta-kg
Service KP milestone dashboard: https://github.com/orgs/biothings/projects/5
Federated querying of BioThings APIs, done in 2 steps:
- Build a query path plan defining APIs relevant to answer the query
- Execute the query path plan to retrieve data from the different APIs.
- BioThings Explorer UI demo: https://biothings.io/explorer
- Docs: https://biothings_explorer.readthedocs.io/en/latest
- Jupyter Notebooks on gitHub
Autonomous Relay Agent for Generation Of Ranked Networks. A tool to query Knowledge Providers (KPs) and synthesize highly ranked answers relevant to user-specified questions
- operate in a federated knowledge environment
- bridge the precision mismatch between data specificity in KPs and more abstract level of user queries
- generalize answer ranking
- Question Augmentation
- Answer Coalescence
- ReasonerStdAPI Message Jupyter Notebook visualizer: https://github.com/ranking-agent/gamma-viewer
Team Expander Agent: A tool for enhancing query graphs. ARAX exposes all graph reasoning capabilities within a domain specific language: ARAXi. ARAX is a tool for querying, manipulating, filtering, learning on, and exploring biomedical knowledge graphs.
Analogical reasoning engine
Ranking results through explanations
- Explanatory evidence via NLU model
- Explanatory evidence via other methods
- Explaining information vs. explaining decisions
Visualization of biomedical context
Based on the miniKanren logic programming language for reasoning over Knowledge Graoph (SemMedDB).
- SPOKE: a biomedical knowledge metagraph (~25 sources)
- reasoning to support facts from empirical evidences (Electronic Health Records, multi-omics studies)
- takes query from ARS and extracts a graph q (output graph) from its internal Knowledge Network (SPOKE)
- checks empirical evidence from raw data of cohorts (EHR and multi-omics studies)
- SPOKE on neo4j. See documentation on GitHub.
ARS registry: https://ars.transltr.io/ars/app/status
Jupyter Notebook to combine data from various Knowledge Providers, produced during the Relay Days.