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.
Duke Scholars
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Related Subject Headings
- Treatment Outcome
- Signal Transduction
- Predictive Value of Tests
- Pharmacology & Pharmacy
- Oligonucleotide Array Sequence Analysis
- Models, Statistical
- Humans
- Fatigue Syndrome, Chronic
- Data Interpretation, Statistical
- Asparaginase
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Treatment Outcome
- Signal Transduction
- Predictive Value of Tests
- Pharmacology & Pharmacy
- Oligonucleotide Array Sequence Analysis
- Models, Statistical
- Humans
- Fatigue Syndrome, Chronic
- Data Interpretation, Statistical
- Asparaginase