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By Sorensen D.C., Wets R.J.-B. (eds.)

ISBN-10: 364200847X

ISBN-13: 9783642008474

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Extra resources for Algorithms and Theory in Filtering and Control, part 1

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In addition to genes clustering, tissues clustering are valuable in identifying samples that are in the different disease states, discovering or predicting different cancer types, and evaluating the effects of novel drugs and therapies [1, 31, 70]. Golub et al. described the restriction of traditional cancer classification methods and divided cancer classification as class discovery and class prediction. They utilized SOFM to discriminate two types of human acute leukemias: acute myeloid leukemia (AML) and acute lymphoblastic leukemia Computational Intelligence in Clustering Algorithms 41 Fig.

5 dB) is achieved with one tri-variate linear polynomial using our mapping. References 1. H. R. Hilbert: Estimation of linear functionals on Sobolev spaces with applications to Fourier transforms and spline interpolation. SIAM J. Numerical Analysis 7, 1970, 113–124. 2. M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf: Computational Geometry Algorithms and Applications. Springer, 1997. 3. S. Dekel and D. Leviatan: The Bramble-Hilbert lemma for convex domains. SIAM J. Math. Anal. 35, 2004, 1203–1212.

For convenience, a begin state (B) and an end (E) state are added to the model. Either 4-letter nucleotide alphabets or 20-letter amino acid alphabets are generated from match and insert states according to some emission probability distributions. Delete states do not produce any symbols, and are used to skip the match states. K HMMs are required in order to describe K clusters, or families (subfamilies), which are regarded as a mixture model and proceeded with an EM learning algorithm. This paradigm models clusters directly from original data without additional process that may cause information loss.

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