New developments in cancer-related computational statistics.
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.
Rosenfeld
Simon
S
Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, EPN 3136, 6130 Executive Boulevard, Rockville MD 20892, USA. sr212a@nih.gov
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D015894:Genome, Human; D006801:Humans; D008390:Markov Chains; D009369:Neoplasms / Q000235:genetics / Q000517:prevention & control*; D017711:Nonlinear Dynamics