Chapters in this part of the book address tasks common in the downstream analysis (after preprocessing) of high-dimensional data. The basic assumption is that preprocessing has led to a sample for which it is reasonable to make comparisons between samples or between feature-vectors assembled across samples. Most examples are based on microarray data, but the principles are much broader and apply to many other sources of data. In this overview, the basic concepts and assumptions are briefly sketched.
KeywordsMicroarray Data Expression Measure Array Comparative Genomic Hybridization cDNA Array Computational Learning Theory
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