Improved prediction of treatment response using microarrays and existing biological knowledge.
A desired application for microarrays in the clinic is to predict treatment response from an often diverse patient population. We present a method for analyzing microarray data that is predicated on biological pathway and function knowledge as opposed to a purely data-driven initial analysis. From an analysis perspective, this methodology takes advantage of information that is available across genes on a single array, as well as differences in those patterns across measurements. By using biological knowledge in the initial analysis, the accuracy and robustness of microarray profile classification is enhanced, especially when low numbers of samples are available. For clinical studies, particularly Phase I or I/II studies, this technique is exceptionally advantageous.
Lin
Simon M
SM
Northwestern University, Robert H Lurie Cancer Center, Chicago, IL 60611, USA. S-Lin2@northwestern.edu
Devakumar
Jyothi
J
Kibbe
Warren A
WA
D001215:Asparaginase / Q000235:genetics; D003627:Data Interpretation, Statistical; D015673:Fatigue Syndrome, Chronic / Q000145:classification / Q000235:genetics* / Q000628:therapy*; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D015398:Signal Transduction / Q000235:genetics; D016896:Treatment Outcome
D001215:Asparaginase