Text and data mining

Text and data mining

• Textmining phenotypes (Rob H, Hongyan Wu, Yas; interested: S Kawashima, Tudor, R Vos, Mark W, S Kumagai, MichelD, interested: Joe Miyamoto)
  • Drug interactions and phenotypes
  • complex phenotypes
  • environment-phenotype interactions
  • host-pathogen interactions and phenotypes for infectious diseases
  • microbial phenotypes, environments, genetic information (add to MicrobeDB?)
  • Create a X Phenotype Ontology, X \in { frog, chicken, mosquito }
• Natural language processing, literature annotation and QA system
  • Interoperability of text mining/annotation resources (Tudor, interested: Takatomo, E. Bolton, Takeru Nakazato, Raoul, MarkT)
    • Goal
      • to collect annotations to Biomedical Literature
      • to align them
      • to make them publicly accessible
        • REST API
        • SPARQL endpoint
      • to find and develop appliations
    • Resources
      • PubTator REST API to become compatible with PubAnnotation Annotation Server API (Wei)
      • Phenotype annotation to be registered to PubAnnotation (Tudor)
      • TextAE to be used in Patient Archive and Orphanet Knowledge Management (Tudor)
      • DisGeNet annotation to be registered to PubAnnotation (Núria, Tudor)
      • EuropeanPMC - PubAnnotation Interoperability (Jee-Hyub)
      • Nanotea (Alex)
      • PubAnnotation - an open repository of literature annotation
  • Knowledge Graph Annotator for human curation (MarkT, Jee-Hyub, interested: E. Bolton)
    • Finished: bug fixes and working nanopub store interface
    • New feature ideas (many thanks to Nuria and Erick):
      • more authentication options, e.g. LinkedIn, Twitter, Scopus, ResearchID
      • options to store annotation (nanopub) in different locations
      • register type of evidence with ECO ontology, or
      • register source of evidence with PubAnnotation URL
    • In progress: RDFa bookmarklet to connect to the annotator from any html page
  • (Graph-based) data analytics on top of integrated text mining data sources (disease - gene - phenotype - chemical entities - species) (Tudor, interested: E. Bolton, Atsuko, Joe Miyamoto)
  • QA over LOD
    • Bio2RDF+UniProt setup for LODQA (Michel, Hongyan, Jin-Dong, Jerven, interested: E. Bolton)

Day 1&2

Text mining annotation interoperability (Text mining annotation alignment and analysis)

• Motivation
  • Various annotation projects sharing the same target, PubMed and PMC.
  • They are maintained in silos.
• Goal
  • To collect annotations to literature and align them
  • To estabilish interoperability between text annotation resources
  • To make them publicly accessible through dereferenceable URIs, REST API, RDF and SPARQL endpoints
  • To find and develop applications
• Participants
  • Wei, Tudor, Núria, Mark T., Jee-Hyub, Kevin, Alex G, Jin-Dong ...
• Started integration of several text mined data sets:
  • DisGeNET (diseases + genes)
  • PubTator (diseases + genes + variants + species)
  • HPO Pubmed annotations
  • DisGeRel? (diseases + genes)
• PubAnnotation to provide alignment and storage of annotation resources
• API-level interoperability
  • PubTator is interoperable with PubAnnotation at API level.
  • Phenotype CR
• Issues
  • How to represent document level annotation.
  • How to align concept labels (ontology alignment problem).
  • How to do the quality assessment.
• Goals:
• Naive comparison of text mined concepts - for quality purposes
• Detection of gaps between mined data and curated domain knowledge with focus on disease - gene - phenotype associations (e.g., inferring new phenotypes for rare disorders)
• Clustering of genes based on phenotypes and / or phenotypes based on genes - and perhaps going forward towards BPs | MFs, etc ... (integration with GO)

Day 4

API-level interoperability (con.)

• Manual curated MESH term annotation
  • Integrated the MeSH-PMIDs manual curated annotation in PubAnnotation.
    • E-utilites for accessing the title/abstract and MESH terms.
      • http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/PubAnnotationByMeSH.cgi
      • http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/PubAnnotationByPubTator.cgi
    • Disease/Chemical are recognized as two individual categories, otherwise are represented by the general category: "MESH".
    • Gang Fu used SPARQL to extract MeSH synonyms for string match.
    • Exact match with simple pre- and post-processing.
  • To-Do: separates genes, species as two individual categories from the general category.
  • Ex. http://textae.pubannotation.org/editor.html?mode=edit&target=http://pubannotation.org/projects/Test_MeSH/docs/sourcedb/PubMed/sourceid/9916105/annotations.json

Textmining phenotypes

• Drug labels and human/mouse/rat phenotypes: first attempt at http://aber-owl.net/aber-owl/diseasephenotypes/drugs/