Adaptive Information Disclosure (http://adaptivedisclosure.org)
Collaboration within the Dutch Virtual Laboratory e-Science project (http://www.vl-e.nl)
Created at: Wednesday 07 November 2007 @ 18:45:12 (GMT)
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Created: 21/03/08 @ 19:00:12 | Last updated: 23/03/08 @ 10:39:27
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
A movie to show the principle of the round trip from a biology question via pieces of code wrapped as web services and combined into a workflow (computational experiment) in Taverna, via uploading to myExperiment and back to the biologist through myExperiment's run facility. NB: at the time of uploading this movie the runner-option was in test-phase.
It is important to note that the workflow combines the work of various scientists with different expertise and some at remote locations around ...
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Created: 10/05/10 @ 16:21:09 | Last updated: 12/01/12 @ 14:39:37
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
The workflow extracts protein names from documents retrieved from MedLine based on a user Query (cf Apache Lucene syntax). The protein names are filtered by checking if there exists a valid UniProt ID for the given protein name.
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Created: 16/05/09 @ 01:06:26 | Last updated: 16/05/09 @ 01:13:18
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License: Creative Commons Attribution-No Derivative Works 3.0 Unported License
This workflow is for demonstration purposes only. Please contact the authors if you wish to try it. We will gladly collaborate with you.
Summary
This workflow extracts proteins and protein relations from Medline. Extracted protein names (symbols of at least 3 characters) are validated against mouse, rat, and human UniProt symbols, so the results are limited to these species. This workflow follows the following basic steps:
it retrieves documents relevant for the query string
i...
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Created: 20/03/09 @ 15:20:36 | Last updated: 20/03/09 @ 15:22:25
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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Created: 28/05/09 @ 12:21:05
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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...
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Created: 10/12/07 @ 23:10:00
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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)...
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Created: 10/12/07 @ 22:17:00
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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.
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Created: 10/12/07 @ 22:15:54 | Last updated: 10/12/07 @ 22:45:49
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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.
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Created: 10/12/07 @ 22:14:26
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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, ...
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Created: 10/12/07 @ 22:13:35
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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Created: 10/12/07 @ 22:12:00
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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Created: 10/12/07 @ 21:48:33 | Last updated: 10/12/07 @ 22:54:42
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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 ...
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Created: 15/11/07 @ 09:40:24
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
This workflow discovers proteins from plain text and adds synonyms using Martijn Schuemie's proteins synonym service. Proteins are discovered with the AIDA 'Named Entity Recognize' web service by Sophia Katrenko (service based on LingPipe), from which output it filters out proteins. The Named Recognizer services uses the pre-learned genomics model, named 'MedLine', to find genomics concepts in plain text.
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Created: 15/11/07 @ 08:58:00 | Last updated: 15/11/07 @ 09:12:34
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
This workflow discovers proteins from plain text. It is built around the AIDA 'Named Entity Recognize' web service by Sophia Katrenko (service based on LingPipe), from which output it filters out proteins. The Named Recognizer services uses the pre-learned genomics model, named 'MedLine', to find genomics concepts in plain text.
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Created: 03/10/07 @ 18:36:12 | Last updated: 15/11/07 @ 09:02:44
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
COMPETITION:
For friends only:
If you find any two topics that return true positives with this workflow I will buy you a bottle of wine (or equivalent).
Terms: if we confirm that the protein was indeed never mentioned together with both input topics in one article, we will publish this together.
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This workflow implements Swanson's prinicple with services from the AIDA toolbox. It tries to find proteins that link two topics, while they never mentioned together with both topics in ...
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Created: 15/12/08 @ 20:46:09 | Last updated: 11/08/11 @ 09:22:23
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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 Apa...
Rating: 4.0 / 5 (2 ratings) | Versions: 4 | Reviews: 0 | Comments: 3 | Citations: 0 Viewed: 3861 times | Downloaded: 576 times Tags (9): |
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Created: 14/11/07 @ 12:47:57 | Last updated: 15/11/07 @ 09:00:44
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
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 ...
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Created: 03/10/07 @ 18:36:10 | Last updated: 13/11/07 @ 23:47:41
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
This workflow uses Martijn Schuemie's protein synonym service to produce synonyms and a new query string from the input query term. The service is limited to proteins, enzymes and genes. An input query that is a boolean string will be split and processed, but the boolean logic of the input query will be lost.
Workflow URL:
http://rdf.adaptivedisclosure.org/~marco/BioAID/Public/Workflows/BioAID/ProteinSynonymsToQuery.xml
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