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BioAID_ProteinDiscovery_filterOnHumanUnipr... (11)

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This workflow finds proteins relevant to the query string via the following steps: A user query: a single gene/protein name. E.g.: (EZH2 OR "Enhancer of Zeste"). Retrieve documents: finds 'maximumNumberOfHits' relevant documents (abstract+title) based on query (the AIDA service inside is based on Apache's Lucene) Discover proteins: extract proteins discovered in the set of relevant abstracts with a 'named entity recognizer' trained on genomic terms using a Bayesian approach; the AIDA serv...

Created: 2009-05-28

Credits: Marco Roos Martijn Schuemie AID AID_myGrid_collaboration

Attributions: BioAID_DiseaseDiscovery_RatHumanMouseUniprotFilter

Retrieve_bio_documents (2)

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This workflow retrieves relevant documents, based on a query optimized by adding a string to the original query that will rank the search output according to the most recent years. The added string adds years with priorities (most recent is highest); it starts at 2007.

Created: 2007-12-10 | Last updated: 2007-12-10

Credits: Marco Roos Edgar AID

Extract_proteins (2)

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This workflow filters protein_molecule-labeled terms from an input string(list). The result is a tagged list of proteins (disregarding false positives in the input). Internal information: This workflow is a copy of 'filter_protein_molecule_MR3' used for the NBIC poster (now in Archive).

Created: 2007-12-10 | Last updated: 2007-12-10

Credits: Marco Roos

Discover_entities (2)

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This workflow contains the 'Named Entity Recognize' web service from the AIDA toolbox, created by Sophia Katrenko. It can be used to discover entities of a certain type (determined by 'learned_model') in documents provided in a lucene output format. Known issues: The output of NErecognize contains concepts with / characters, breaking the xml. For post-processing its results it is better to use string manipulation than xml manipulations. The output is per document, which means entities will ...

Created: 2007-12-10 | Last updated: 2007-12-10

Credits: Marco Roos Sophia katrenko AID

Demo_DiseaseDiscovery_byHumanUniprot_scaffold (1)

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This workflow finds disease relevant to the query string via the following steps: A user query: a list of terms or boolean query - look at the Apache Lucene project for all details. E.g.: (EZH2 OR "Enhancer of Zeste" +(mutation chromatin) -clinical); consider adding 'ProteinSynonymsToQuery' in front of the input if your query is a protein. Retrieve documents: finds 'maximumNumberOfHits' relevant documents (abstract+title) based on query (the AIDA service inside is based on Apache's Lucene)...

Created: 2007-12-10

Credits: Marco Roos AID

Retrieve_documents_MR1 (1)

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This workflow applies the search web service from the AIDA toolbox. Comments: This search service is based on lucene defaults; it may be necessary to optimize the querystring to adopt the behaviour to what is most relevant in a particular domain (e.g. for medline prioritizing based on publication date is useful). Lucene favours shorter sentences, which may be bad for subsequent information extraction.

Created: 2007-12-10

Credits: Marco Roos Edgar AID

Lucene_bioquery_optimizer_MR1 (1)

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This workflow does four things: it retrieves documents relevant for the query string it discovers entities in those documents, these are considered relevant entities it filters proteins from those entities (on the tag protein_molecule) it removes all terms from the list produced by 3 (query terms temporarily considered proteins) ToDo Replace step 4 by the following procedure: 1. remove the query terms from the output of NER (probably by a regexp matching on what is inside the tag, ...

Created: 2007-12-10

Credits: Marco Roos AID

Link_protein_to_OMIM_disease (1)

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Created: 2007-12-10

Credits: Marco Roos AID

Flatten_and_make_unique (1)

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Created: 2007-12-10

Credits: Marco Roos AID