Sequence clustering with the Self-Organizing Hidden Markov Model Map

Abstract

A hybrid approach combining the self-organizing map (SOM) and the hidden Markov model (HMM) is presented. The self-organizing hidden Markov model map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are blended together in an attempt to meet the increasing requirements imposed by the deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. Addressing many of the most intriguing biological sequence analysis problems is achieved through its automatic raw sequence data learning mechanism. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. A comprehensive series of experiments based on the globin protein family demonstrates SOHMMMpsilas sophisticated characteristics and advanced capabilities.