By Jennifer E. Van Eyk, Michael J. Dunn

ISBN-10: 3527305963

ISBN-13: 9783527305964

ISBN-10: 3527605452

ISBN-13: 9783527605453

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Additional resources for Proteomic and Genomic Analysis of Cardiovascular Disease

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Pac. Symp. Biocomput. 2000, 418–429. Butte AJ, Tamayo P, Slonim D, Golub TR, et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA. 2000; 97(22):12182–12186. Lele S, Richtsmeier JT. Euclidean distance matrix analysis: confidence intervals for form and growth differences. Am. J. Phys. Anthropol. 1995; 98(1):73–86. Hartigan JA, Wong MA. A K-Means Clustering Algorithm. Applied Statistics 1979; 28:100–108.

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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Proteomic and Genomic Analysis of Cardiovascular Disease by Jennifer E. Van Eyk, Michael J. Dunn

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