Breast Tumor Susceptibility to Chemotherapy via Support Vector Machines
Abstract
Support 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.
Subject
DNA macroarrays
chemotherapy
breast cancer
support vector machines
Permanent Link
http://digital.library.wisc.edu/1793/64326Citation
03-06