title: Self-Organizing Hidden Markov Model Map (SOHMMM).
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 fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. 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. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities.
authors.affiliation: Intelligent Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Athens, Greece. Electronic address: christos.ferles@gmail.com.
authors.forename: Christos
authors.initials: C
authors.lastname: Ferles
authors.forename: Andreas
authors.initials: A
authors.lastname: Stafylopatis
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001483:Base Sequence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003199:Computer Systems; D004247:DNA / Q000737:chemistry; D008390:Markov Chains; D015233:Models, Statistical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry; D012313:RNA / Q000737:chemistry; D015203:Reproducibility of Results
chemical_list: D011506:Proteins; D012313:RNA; D004247:DNA
keywords: Clustering; DNA/RNA/protein sequences; Hidden Markov Model (HMM); Self-Organizing Map (SOM); Spatiotemporal; Unsupervised learning