ABSTRACT
This chapter describes the use of mixture models in genetic and genomics is presented by increasing complexity of the latent structure. It considers applications with independent latent variable structures to genome and transcriptome analysis. The chapter shows that the use of hidden Markov models in genomics, presenting a variety of problems with their associated translation in terms of emission distributions and hidden states. It introduces more complex dependency structures used in genomics such as the hidden Markov random field and stochastic block model with their associated parameter estimation difficulties. Mixture models are intensively used in genetics and genomics either for identifying latent structures or for modeling densities. The DNA microarray technology is thus of great importance in many applications such as functional genomics, medical and clinical diagnosis, drug discovery, targeting, and monitoring. any diseases are associated with genomic alterations which consist of either the loss or the amplification of some regions of the genome.
