Lymphoma type prediction based on microarray data
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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Statistics
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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Created: 2009-11-23
Credits:
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