Benjamin I. P. Rubinstein
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Abstract
Machine learning and data mining have found a multitude of successful applications in microarray analysis, with gene clustering and classification of tissue samples being widely cited examples. Low-level microarray analysis -- often associated with the pre-processing stage within the microarray life-cycle -- has increasingly become an area of active research, traditionally involving techniques from classical statistics. This paper explores opportunities for the application of machine learning and data mining methods to several important low-level microarray analysis problems: monitoring gene expression, transcript discovery, genotyping and resequencing. Relevant methods and ideas from the machine learning community include semi-supervised learning, learning from heterogeneous data, and incremental learning.
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- Machine learning in low-level microarray analysis
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