• Bayesian networks;
  • expression profiles;
  • microarray;
  • multiple testing;
  • hidden Markov chain;
  • support vector machines;
  • optimization

Abstract: 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.