Version 3 (latest)
(of 3)
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Version created on:
05/12/07 @ 23:11:42
by:
Marco Roos
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Revision comments
Last edited on: 15/05/08 @ 21:10:32 by: Marco Roos
Title: BioAID_DiseaseDiscovery_byHumanUniprot
Type: Taverna 1
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Description
This workflow finds disease relevant to the query string via the following steps: 1. 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. 2. Retrieve documents: finds ‘maximumNumberOfHits’ relevant documents (abstract+title) based on query (the AIDA service inside is based on Apache’s Lucene) 3. 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 service inside is based on LingPipe. This subworkflow also ‘filters’ false positives from the discovered protein by requiring a discovery has a valid UniProt ID. Martijn Schuemie’s service to do that contains only human UniProt IDs, which is why this workflow only works for human proteins. 4. Link proteins to disease contained in the OMIM disease database (with a service from Japan that interrogates OMIM)
Workflow by Marco Roos (AID = Adaptive Information Disclosure, University of Amsterdam; http://adaptivedisclosure.org)
Text mining services by Sophia Katrenko and Edgar Meij (AID), and Martijn Schuemie (BioSemantics, Erasmus University Rotterdam). OMIM service from the Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, director Hideaki Sugawara (see http://xml.nig.ac.jp)
Changes to our original BioAID_DiseaseDiscovery workflow: * Use of Martijn Schuemie’s synsets service to * provide uniprot ids to discovered proteins * filter false positive discoveries, only proteins with a uniprot id go through; this introduces some false negatives (e.g. discovered proteins with a name shorter than 3 characters) * solve a major issue with the original workflow where some false positives could contribute disproportionately to the number of discovered diseases * Counting of results in various ways.
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Option 1:
Note: you need to have both the WHIP Launcher and the Taverna myExperiment/WHIP plugin installed on your machine for this to work. See here for information.
Option 2:
Copy and paste this link into File > 'Open workflow location...'
http://www.myexperiment.org/workflows/110/download?version=3
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Workflow Components
All versions of this Workflow are licensed under the Creative Commons Attribution-Share Alike 3.0 License.
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From biologist to web service to Taverna to myExperiment to biologist
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Earliest Version:
[1] - BioAID_DiseaseDiscovery_byHumanUniprot
Previous Versions:
[2] - BioAID_DiseaseDiscovery_byHumanUniprot
Latest Version:
[3] - BioAID_DiseaseDiscovery_byHumanUniprot
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Copyright (c) 2007 - 2008 The University of Manchester and University of Southampton
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