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dc.contributor.authorMangasarian, Olvi
dc.contributor.authorFung, Glenn
dc.date.accessioned2013-01-17T17:50:40Z
dc.date.available2013-01-17T17:50:40Z
dc.date.issued2003
dc.identifier.citation03-06en
dc.identifier.urihttp://digital.library.wisc.edu/1793/64326
dc.description.abstractSupport vector machines (SVMs), utilizing RNA signature measurements, were used to generate a classi er to distinguish breast cancer patients that are partial-responders to chemotherapy treatment, from patients that are nonresponders. Partial responders are patients whose tumors were reduced by at least 50%. A stand-alone linear-programmingbased SVM algorithm was used to separate the partial-responders from the nonresponders. A novel aspect of the classi cation approach utilized here is that each patient is represented by multiple points (replicates) in the 25-dimensional input space of RNA signature measurements. Replicates for all patients except those for one patient, were used as a training set. The average of the replicates for the patient left out was then used to test the leave one out correctness (looc). The looc for a group of 35 patients, with 9 partial-responders and 26 nonresponders was 94.2%, in an input space of 5 RNA measurements extracted from an original space of 25 RNA measurements.en
dc.subjectDNA macroarraysen
dc.subjectchemotherapyen
dc.subjectbreast canceren
dc.subjectsupport vector machinesen
dc.titleBreast Tumor Susceptibility to Chemotherapy via Support Vector Machinesen
dc.typeTechnical Reporten


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    DMI Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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