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Items tagged with "bioassist_nl" (11)

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Groups (2)
Owner

Network-member e-science support team for NBIC

Unique name: eScienceSupportTeam4NBIC
Created: Sunday 16 March 2008 22:23:11 (UTC)

This group brings together scientists with expertise in both (medical) bioinformatics and e-science. The members can give advice to bioinformaticians who want to adopt an 'e-science approach'. They want to share their expertise and experience for the application of technologies such as workflow, web/biomoby services, grid, semantic web, etcetera. The group was founded in particular for the Du...

3 shared items   |   0 announcements

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Owner

Network-member BioSemantics Privileged Users @ Human Gene...

Unique name: LeidenBioSemanticsUsers
Created: Wednesday 23 September 2009 01:10:24 (UTC)

Group of colleagues at the Human Genetics Department of the Leiden University Medical Centre and closely collaborating groups who are interested to be prime users of new developments in BioSemantics and e-Science, and in return are willing to provide feedback and suggestions. See http://biosemantics.org for more information about the BioSemantics collaboration in the Netherlands.

15 shared items   |   1 announcements

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Workflows (9)

Workflow 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: User Marco Roos User Sophia katrenko Network-member AID

Workflow 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: User Marco Roos

Workflow Flatten_and_make_unique (1)

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No description

Created: 2007-12-10

Credits: User Marco Roos Network-member AID

Workflow Link_protein_to_OMIM_disease (1)

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No description

Created: 2007-12-10

Credits: User Marco Roos Network-member AID

Workflow 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: User Marco Roos Network-member AID

Workflow 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: User Marco Roos User Edgar Network-member AID

Workflow 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: User Marco Roos User Edgar Network-member AID

Workflow 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: User Marco Roos Network-member AID

Workflow 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: User Marco Roos User Martijn Schuemie Network-member AID Network-member AID_myGrid_collaboration

Attributions: Workflow BioAID_DiseaseDiscovery_RatHumanMouseUniprotFilter

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