Workflow Entry: BioAID_ProteinToDiseases

Created at: 14/11/07 @ 12:47:57 Last updated: 15/11/07 @ 09:00:44

Version 1 (of 1)

Version created on: 14/11/07 @ 12:47:57 by: Marco Roos | Revision comments Expand

Last edited on: 15/11/07 @ 09:00:44 by: Marco Roos

Title: BioAID_ProteinToDiseases

Type: Taverna 1

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Description

This workflow was based on BioAID_DiseaseDiscovery, changes: expects only one protein name, adds protein synonyms).

This workflow finds diseases relevant to the query string via the following steps:

A user query: a single protein name
Add synonyms (service courtesy of Martijn Scheumie, Erasmus University Rotterdam)
Retrieve documents: finds relevant documents (abstract+title) based on query
Discover proteins: extract proteins discovered in the set of relevant abstracts

5. Link proteins to disease contained in the OMIM disease database.

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Run

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Option 1:

Launch in Taverna

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/75/download?version=1
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Workflow Components

Inputs (1)

Name	Description
input_protein	One protein or enzyme name or acronym. A protein synonym service will provide synonyms (courtesy of Martijn Scheumie, Erasmus University Rotterdam). Example: EZH2 Advanced information: Internally the input query is used by an AIDA search service based on the open source information retrieval software Lucene. Please look at the Apache Lucene documentation for details on lucene queries. For this workflow one protein or enzyme name is expected; the behaviour for more elaborate queries is undefined.

Processors (10)

Name	Type	Description
search_field	stringconstant
Document_index	stringconstant
maxHits	stringconstant
Flatten_and_make_unique	workflow
Remove_xml_tag	beanshell
Retrieve_documents	workflow	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.
Discover_proteins	workflow	This workflow applies the discovery workflow built around the AIDA 'Named Entity Recognize' web service by Sophia Katrenko. It uses the pre-learned genomics model, named 'MedLine', to find genomics concepts in a set of documents in lucene output format.
Link_proteins_to_diseases	workflow
Add_protein_synonyms	workflow
Flatten_list	local

Beanshells (3)

Name	Inputs	Outputs
Prioritise_lucene_query	query_string priority_string	lucene_query
label_OMIM_disease	OMIM_disease_string	OMIM_disease_label
Remove_xml_tag	tagged_term	term

Outputs (1)

Name	Description
discovered_diseases

Links (11)

Source	Sink
Discover_proteins:discovered_proteins	Remove_xml_tag:tagged_term
Document_index:value	Retrieve_documents:document_index
Link_proteins_to_diseases:OMIM_disease_label	Flatten_and_make_unique:input
Remove_xml_tag:term	Link_proteins_to_diseases:keyword
Retrieve_documents:relevant_documents	Discover_proteins:documents_from_lucene
input_protein	Add_protein_synonyms:query_term
Add_protein_synonyms:new_query	Retrieve_documents:query_string
Flatten_and_make_unique:flattened_unique_output	Flatten_list:inputlist
Flatten_list:outputlist	discovered_diseases
maxHits:value	Retrieve_documents:maxHits
search_field:value	Retrieve_documents:search_field