Sequence clustering with the self-organizing hidden markov model map

Ferles, C; Stafylopatis, A

dc.contributor.author	Ferles, C	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:45:47Z
dc.date.available	2014-03-01T02:45:47Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32389
dc.subject	Cross Section	en
dc.subject	deoxyribonucleic acid	en
dc.subject	Dimensional Reduction	en
dc.subject	Hybrid Approach	en
dc.subject	Large Scale	en
dc.subject	Prior Knowledge	en
dc.subject	Protein Family	en
dc.subject	ribonucleic acid	en
dc.subject	Self Organization	en
dc.subject	Self Organized Map	en
dc.subject	Sequence Analysis	en
dc.subject	Sequence Space	en
dc.subject	Theoretical Foundation	en
dc.subject	Experience Base	en
dc.subject	Hidden Markov Model	en
dc.subject.other	Algorithmic realization	en
dc.subject.other	Biological sequence analysis	en
dc.subject.other	Deoxyribonucleic acids	en
dc.subject.other	Dimensionality reduction	en
dc.subject.other	Hybrid approach	en
dc.subject.other	Large-scale sequences	en
dc.subject.other	Learning mechanism	en
dc.subject.other	Prior knowledge	en
dc.subject.other	Probabilistic sequences	en
dc.subject.other	Protein chains	en
dc.subject.other	Protein family	en
dc.subject.other	Ribonucleic acid	en
dc.subject.other	Self organizing	en
dc.subject.other	Sequence data	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Conformal mapping	en
dc.subject.other	DNA	en
dc.subject.other	Education	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Nucleic acids	en
dc.subject.other	Object recognition	en
dc.subject.other	Organic acids	en
dc.subject.other	RNA	en
dc.subject.other	Cluster analysis	en
dc.title	Sequence clustering with the self-organizing hidden markov model map	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696720	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696720	en
heal.identifier.secondary	4696720	en
heal.publicationDate	2008	en
heal.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 SOHMMM's sophisticated characteristics and advanced capabilities.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696720	en