By Millikan R. A.

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Cancer Inst. 1999; 91(5):453–459. Ben-Dor A, Bruhn L, Friedman N, Nachman I, et al. Tissue classification with gene expression profiles. J. Comput. Biol. 2000; 7(3–4):559–583. Tamayo P, Slonim D, Mesirov J, Zhu Q, et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA. 1999; 96(6):2907–2912. Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps.

Within the scope of this chapter, we can only briefly describe some of the most commonly used techniques. This overview can by no means be comprehensive, because new tools and algorithms are continuously being developed. 2 Computational Analysis of Microarray Data ples or diseases), and un-supervised learning (analysis looking for characterization of the components of a data set, without a priori input on cases or genes). In supervised learning, algorithms attempt to learn a concept from labeled training examples, to predict the labels of the test set correctly.

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Relations of Field-Currents to Thermionic-Currents by Millikan R. A.


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