Journal
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
Volume 5, Issue 2, Pages 189-208Publisher
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJDMB.2011.039177
Keywords
feature filter; microarray classification; multi-task learning; transfer learning; bioinformatics
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Funding
- NSF [IIS-0546155]
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Leveraging information from the publicly accessible data repositories can be very useful when training a classifier from a small-sample microarray data. To achieve this, we proposed a multi-task feature selection filter that borrows strength from auxiliary microarray data. It uses Kruskal-Wallis test on auxiliary data and ranks genes based on their aggregated p-values. The top-ranked genes are selected as features for the target task classifier. The multi-task filter was evaluated on microarray data related to nine different types of cancers. The results showed that the multi-task feature selection is very successful when applied in conjunction with both single-task and multi-task classifiers.
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