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Tag: rdf Licence: by-nd

Workflow BioAID_EnirchBioModelWithProteinsFromText (7)

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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...

Created: 2009-05-16 | Last updated: 2009-05-16

Credits: User Marco Roos User Sophia katrenko User Andrew Gibson User M. Scott Marshall User Willem van Hage User Edgar User Martijn Schuemie Network-member AID

Workflow Mining Semantic Web data using Corresponde... (1)

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This workflow will explain that how an example set can be extracted from an RDF resource using the provided SPARQL query. This example set is then divided into training and test parts. These sub-example sets are used by the Correspondencce Analysis operators (encapsulate the Correspondencce Analysis data transformation technique) which processes each feature at a time and transform the data into a different space. This transformed data is more meaningful and helps the learner to improve clas...

Created: 2011-06-25 | Last updated: 2011-06-25

Workflow Mining Semantic Web data using Corresponde... (1)

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This workflow describes how to learn from the Semantic Web's data using a data transformation algorithm 'Correspondence Analysis'. The input to the workflow is a feature vector developed from a RDF resource. The loaded example set is divided into training and test parts. These sub-example sets are used by the Correspondence Analysis operators (encapsulate the Correspondence Analysis data transformation technique) which processes each feature at a time and transform the data into a different...

Created: 2011-06-25 | Last updated: 2011-06-25

Workflow Mining Semantic Web data using FastMap - E... (1)

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This workflow describes how to learn from the Semantic Web's data. The input to the workflow is a feature vector developed from a RDF resource. The loaded example set is then divided into training and test parts. These sub-example sets are used by the FastMap operators (encapsulate the FastMap data transformation technique), which processes each feature at a time and transform the data into a different space. This transformed data is more meaningful and helps the learner to improve classfica...

Created: 2011-06-25 | Last updated: 2011-06-25

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