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InviteWorkflow Entry: Lymphoma type prediction based on microarray data
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 Version 7 (latest)
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Version created on:
11/05/10 @ 19:04:30
by:
Wei Tan
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Title: Lymphoma type prediction based on microarray data
Type: Taverna 2
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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.
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1. Wei Tan, Ravi Madduri, Alexandra Nenadic, Stian Soiland-Reyes, Dinanath Sulakhe, Ian Foster, Carole A. Goble, caGrid Workflow Toolkit: A Taverna based workflow tool for cancer Grid, BMC Bioinformatics, 02 November 2010, http://www.biomedcentral.com/1471-2105/11/542, Accessed at: 03 September 2011
2. caArray, Experiment data: Diffuse large B-cell lymphoma outcome prediction , 23 April 2009, https://array.nci.nih.gov/caarray/project/golub-00095, Accessed at: 23 April 2009
3. MA Shipp et al, Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning, NATURE MEDICINE, 23 January 2002, http://www.broad.mit.edu/mpr/publications/projects/Lymphoma/Shipp_et_al_2002.pdf, Accessed at: 23 April 2009
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[1] - Lymphoma type prediction based on microarray data
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[2] - Lymphoma type prediction based on microarray data
[4] - Lymphoma type prediction based on microarray data
[5] - Lymphoma type prediction based on microarray data
[6] - Lymphoma type prediction based on microarray data
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[7] - Lymphoma type prediction based on microarray data
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Original Uploader |
Created: 23/11/09 @ 18:09:47
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
Query all the gene expression data in a caArray experiment. Returns a evenly divided gene expression data set with corresponding class information. They ca be later used as training and test data set in many classification algorithms.Query all the gene expression data in a caArray experiment. Returns a evenly divided gene expression data set with corresponding class information. They can be later used as training and test data set in many classification algorithms.
Rating: 0.0 / 5 (0 ratings) | Versions: 1 | Reviews: 0 | Comments: 3 | Citations: 0 Viewed: 48 times | Downloaded: 19 times Tags (8): |
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Original Uploader |
Created: 24/05/10 @ 22:42:38 | Last updated: 24/05/10 @ 22:42:39
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License: Creative Commons Attribution-Share Alike 3.0 Unported License
preprocess data set using genePattern preProces service, the input should be in genePattern STATML format. Configuration parameters can be adjusted by changing the default preprocess data set using genePattern preProces service, the input should be in genePattern STATML format.preprocess data set using genePattern preProces service, the input should be in genePattern STATML format. Configuration parameters can be adjusted by changing the string constants.
Rating: 0.0 / 5 (0 ratings) | Versions: 2 | Reviews: 0 | Comments: 0 | Citations: 0 Viewed: 59 times | Downloaded: 31 times Tags (5): |
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Non-Information Resource URI: http://www.myexperiment.org/workflows/746
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