Support-Vector-Machine (SVM) based data classification

Created: 2010-05-24 22:34:31 Last updated: 2010-05-24 22:34:32

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Support-Vector-Machine based data classification

Support-Vector-Machine based data classification using genePattern SVM service, the input <data /> should be in genePattern STATML format.

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Titles (1)

Descriptions (2)

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Inputs (4)

Name	Description
testData	test data set
testClass	Class memebership information for test data
trainingClass	class memebership information for training data
trainingData	training data set

Processors (6)

Name	Type	Description
parametersXML10	xmlsplitter
trainingDataXML	xmlsplitter
testClassXML	xmlsplitter
PerformeSVM	wsdl	Wsdl http://node255.broad.mit.edu:6060/wsrf/services/cagrid/SVM?wsdl Wsdl Operation performAnalysis
testDataXML	xmlsplitter
trainingClassXML	xmlsplitter

Beanshells (0)

Outputs (1)

Name	Description
SVM_Result

Datalinks (10)

Source	Sink
trainingDataXML:output	parametersXML10:trainingData
testDataXML:output	parametersXML10:testData
trainingClassXML:output	parametersXML10:trainingClass
testClassXML:output	parametersXML10:testClass
trainingData	trainingDataXML:Data
testClass	testClassXML:ClassMembership
parametersXML10:output	PerformeSVM:parameters
testData	testDataXML:Data
trainingClass	trainingClassXML:ClassMembership
PerformeSVM:parameters	SVM_Result

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Taverna 2

Information Uploader

Wei Tan

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Wei Tan

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4499 viewings

1561 downloads

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Version History

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Support-Vector-Machine (SVM) based data classification

Created by Wei Tan on Monday 23 November 2009 18:16:23 (UTC)
Support-Vector-Machine (SVM) based data classification

Created by Wei Tan on Monday 24 May 2010 22:34:31 (UTC)

Revision comment:

change genepattern SVM service URL to http://node255.broadinstitute.org:6060

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Lymphoma type prediction based on microar... (7)

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Scientific value Using gene-expression patterns associated with DLBCL and FL to predict the lymphoma type of an unknown sample. Using SVM (Support Vector Machine) to classify data, and predicting the tumor types of unknown examples. Steps Querying training data from experiments stored in caArray. Preprocessing, or normalize the microarray data. Adding training and testing data into SVM service to get classification result.

Created: 2010-05-11 | Last updated: 2010-05-11

Credits: Wei Tan Ravi Stian Soiland-Reyes

Support-Vector-Machine (SVM) based data classification

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