Textpresso Central

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, exclusion list
  • External identifiers - which ones? PMIDs, doi's, MOD paper IDs, others?

• 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

• 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
  • 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

• Textpresso Ontology viewer and editor

• Ontology development

Viewing

• 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.

Annotating and Curating

• 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.

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

Currently, for doing this we have:

1) the 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

2) the interaction configuration of the OA

Any others? Ask other WB curators.

• Robust back-end infrastructure with internal Textpresso database holding all annotations

Adapt data models and tables from postgres curation database on tazendra?

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

• Data flow / Transaction model

 - does one big model for all exchanges between all module work?
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