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New Developments in Cancer‐Related Computational Statistics
Author(s) -
ROSENFELD SIMON
Publication year - 2004
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1310.004
Subject(s) - context (archaeology) , computer science , identification (biology) , markov chain , cluster analysis , throughput , systems biology , hidden markov model , computational biology , data science , machine learning , data mining , artificial intelligence , biology , paleontology , botany , telecommunications , wireless
A bstract : A brief overview is presented of recently developed and currently emerging statistical and computational techniques that have been proved to be highly helpful in handling the avalanche of the new type of data generated by modern high‐throughput technologies in experimental biology. The review, in no way comprehensive, focuses attention on Bayesian Networks, Hidden Markov Chain, and methods of chaotic dynamics for time‐course genomic data; innovative methods in optimization and clustering; and multiple testing in the context of identification of differentially expressed genes.