Workflow Entry: BioAID_protein_discovery

Created at: 14/11/07 @ 12:46:33 Last updated: 15/11/07 @ 09:01:13

Version 1 (of 1)

Version created on: 14/11/07 @ 12:46:33 by: Marco Roos | Revision comments Expand

Last edited on: 15/11/07 @ 09:01:12 by: Marco Roos

Title: BioAID_protein_discovery

Type: Taverna 1

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Description

This workflow finds proteins 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 2. Retrieve documents: finds relevant documents (abstract+title) based on query (edit maxHits to change the default maximum number of documents returned) 3. Discover proteins: extract proteins discovered in the set of relevant abstracts.

Services by Edgar Meij and Sophia Katrenko (AID)

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Option 1:

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Option 2:

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http://www.myexperiment.org/workflows/74/download?version=1
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Workflow Components

Inputs (1)

Name	Description
query_string	Query for retrieving document from an indexed corpus. It is assumed the query will be used for a search service based on Lucene. In short that means the query should be string of terms with logical operators or +/- signs to denote if terms are wanted or unwanted. Documents that comply with this query will be used to discover entities in. Examples: EZH2 EZH2 OR "Enhancer of Zeste" +(mutation chromatin) -clinical Please look at the Apache Lucene documentation for details on lucene queries.

Processors (7)

Name	Type	Description
maxHits	stringconstant
Document_index	stringconstant
search_field	stringconstant
Flatten_list	local
Remove_xml_tag	beanshell
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.
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.

Outputs (3)

Name	Description
relevant_documents
discovered_proteins_xml
discovered_proteins

Links (10)

Sink	Source
Retrieve_documents:query_string	query_string
Flatten_list:inputlist	Discover_proteins:discovered_proteins
Retrieve_documents:document_index	Document_index:value
Remove_xml_tag:tagged_term	Flatten_list:outputlist
Discover_proteins:documents_from_lucene	Retrieve_documents:relevant_documents
Retrieve_documents:maxHits	maxHits:value
Retrieve_documents:search_field	search_field:value
discovered_proteins_xml	Flatten_list:outputlist
discovered_proteins	Remove_xml_tag:term
relevant_documents	Retrieve_documents:relevant_documents