Textpresso Central

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General considerations: Specification of data models, markup languages, and flow now is important.

Searching and Category/Ontology Development

  • Control panel: loading papers from existing corpora into a viewer, incorporation of PubMed queries; search results will be used to import full text from PMC or journal site

  • Searches - existing corpora, list of external identifiers, combination of both
    • External identifiers - which ones? PMIDs, doi's, MOD paper IDs, others?
      • Use case: Perform a PubMed search and then port the resulting IDs to a Textpresso search
    • Paper Exclusion List - user supplied, from external data file, e.g. Gene Ontology Annotation File (gaf)
      • Use cases: TAIR CCC black list and filtering based upon papers already annotated with Component term in gaf file

  • Categories and Keywords
    • Organization of categories by task? e.g. GO curation, Phenotype curation, Expression patterns, etc.
    • Create and display category metadata - source, possible use, version, last updated
    • Restrict search to a subset of a category
      • Use case: FlyBase CCC search using only gene names that start with CG
    • How quickly could new searches be performed with modified categories

  • Search Filters
    • Bibliographic filters - year, journal, paper type, etc.
    • Data Type Flagging - NLP results - data models and storage
      • Development of NLP toolbox: pattern matching, statistics, svm, hmm, crf
      • Index of all NLP results for faster querying
      • Display most current precision and recall statistics so users can assess the accuracy of an NLP tool
    • Data Type Flagging - author or curator flags
      • Information stored in postgres on tazendra
    • Textpresso search score cut-off - view scores (mean, range) for curatable papers (this would depend on search criteria)
        • Use case: search all SVM predicted negatives and return those papers with a score >n
    • Curation Status - integration with curation databases - which ones?
    • View previously made annotations - source? tie to a sentence where possible?
      • Robust back-end infrastructure with internal Textpresso database holding all annotations
        • Adapt data models and tables from postgres curation database on tazendra?

  • Textpresso Category and Ontology viewer and editor
    • Stand-alone or interface with curation or both
    • Incorporate statistical analyses (word frequency in positive vs negative sentences, how often is a term the only one from a given category, etc.)

Viewing Search Results

  • Viewer: selecting terms, importing them into OA, prepopulating entries of forms; display results from NLP tools; initiate new NLP analyses (pattern matching, statistical, machine learning)

This will require a uniform representation of all machine learning results w.r.t. papers in Textpresso. Annotation markup language comes to my mind.

  • Viewing options
    • Sort by score, year, journal (like PubMed)
    • See search results within the context of the paper
      • Paper viewer
        • See existing annotations, if tied to a sentence
        • Additional mark-up options? e.g. alleles, reagents, genes

Annotating and Curating

We would need to develop a markup language (XML) for data flows. This should be a generic as possible.

  • OA and its interaction with TC
    • Curators would like to be able to view the search results while curating and make annotations from the true positive sentences.
  • Flag the paper and/or sentence(s) as curatable, relevant but not curatable (sentence only), false positive (break down further?), unannotated - basically TP, FP, FN, TN
    • Have ability to annotate NLP results in bulk
  • Click on a sentence and, depending upon the curation needs, the curation tool is pre-populated with relevant entities.
    • Would need to know species (usually OK for elegans, could be trickier for mammalian species)
  • Click on words to add to category
  • Current Textpresso-based curation forms:
    • CCC (Cellular Component Curation) form
      • Pros:
        • sentences are seen on the same page as annotations
        • form pre-populates curation fields with protein names, category terms, and suggested annotations
        • easy to mark sentences if not curatable
      • Cons:
        • duplicating or making multiple annotations is cumbersome
        • don't see term info for proteins or GO terms
        • don't see additional annotations for proteins mentioned in sentences
    • the interaction configuration of the OA
    • Any others? Ask other WB curators.
  • Add curator comments to a paper, sentence, term
  • Output of curation
    • to Textpresso database
    • to MOD or other project database (e.g., BioGRID)
    • downloadable file - what formats?

Data Models and Flow

  • Integrate Textpresso categories (TCAT), NLP results and curator annotation (CA) into one big data class
 Model needs following elements; not all elements are populated at all times
 - term (TCAT: lexicon entry; NLP: term, sentence identified in paper if applicable; CA: term manually annotated)
 - annotation (TCAT: category term with possible attributes; NLP: machine-learningID or describing term; CA: manual annotation)
 - paper location: PaperID, SentenceID, PosID
 - allowed lexical variations (plural, tenses)
 - ownership (who can change entry)
 - what else?
 - timestamp
 - version
 - source
 - comment
 - possible use

  • Data flow / Transaction model
 - does one big model for all exchanges between all module work?

Action Items 2011-11-29

  • Develop a controlled vocabulary for all items that needs to be query-able:
    • Data type
    • Curation status
    • Possible use
    • Source
    • Type of Annotation
    • Lexical variation
  • Make a first version of the annotation data model
  • Think about how to track changes in tokenization of papers in annotation database
    • When papers get reformatted (sentence identification improve) how to port old annotations
  • Manifestation of Data Model in Textpresso database
    • Import current SVM results
    • Import current HMM results
    • Import current CCC results
    • Import current gene-gene interaction results
    • Import current Molecules results
    • How will category markup go into Textpresso database?
  • Design (and later implement) first version of Textpresso Curator interface