Enhancing the quality of phylogenetic analysis using fuzzy hidden Markov model alignments.

@article{citeulike:1818073, abstract = {Any effective phylogeny inference based on molecular data begins by performing efficient multiple sequence alignments. So far, the Hidden Markov Model (HMM) method for multiple sequence alignment has been proved competitive to the classical deterministic algorithms with respect to phylogenetic analysis; nevertheless, its stochastic nature does not help it cope with the existing dependence among the sequence elements. This paper deals with phylogenetic analysis of protein and gene data using multiple sequence alignments produced by fuzzy profile Hidden Markov Models. Fuzzy profile HMMs are a novel type of profile HMMs based on fuzzy sets and fuzzy integrals, which generalize the classical stochastic HMM by relaxing its independence assumptions. In this paper, alignments produced by the fuzzy HMM model are used in phylogenetic analysis of protein data, enhancing the quality of phylogenetic trees. The new methodology is implemented in HPV virus phylogenetic inference. The results of the analysis are compared against those obtained by the classical profile HMM model and depict the superiority of the fuzzy profile HMM in this field.}, address = {Lab of Medical Informatics, Faculty of Medicine, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece.}, author = {Collyda, C. and Diplaris, S. and Mitkas, P. and Maglaveras, N. and Pappas, C. }, citeulike-article-id = {1818073}, journal = {Medinfo}, keywords = {alignment, alignment\_accuracy, method, phylogeny}, number = {Pt 2}, pages = {1245--1249}, posted-at = {2007-10-25 02:23:22}, priority = {2}, title = {Enhancing the quality of phylogenetic analysis using fuzzy hidden Markov model alignments.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17911914}, volume = {12}, year = {2007} }

TY - JOUR ID - citeulike:1818073 L3 - citeulike-article-id:1818073 TI - Enhancing the quality of phylogenetic analysis using fuzzy hidden Markov model alignments. JF - Medinfo VL - 12 IS - Pt 2 SP - 1245 EP - 1249 AD - Lab of Medical Informatics, Faculty of Medicine, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece. N2 - Any effective phylogeny inference based on molecular data begins by performing efficient multiple sequence alignments. So far, the Hidden Markov Model (HMM) method for multiple sequence alignment has been proved competitive to the classical deterministic algorithms with respect to phylogenetic analysis; nevertheless, its stochastic nature does not help it cope with the existing dependence among the sequence elements. This paper deals with phylogenetic analysis of protein and gene data using multiple sequence alignments produced by fuzzy profile Hidden Markov Models. Fuzzy profile HMMs are a novel type of profile HMMs based on fuzzy sets and fuzzy integrals, which generalize the classical stochastic HMM by relaxing its independence assumptions. In this paper, alignments produced by the fuzzy HMM model are used in phylogenetic analysis of protein data, enhancing the quality of phylogenetic trees. The new methodology is implemented in HPV virus phylogenetic inference. The results of the analysis are compared against those obtained by the classical profile HMM model and depict the superiority of the fuzzy profile HMM in this field. KW - alignment KW - alignment_accuracy KW - method KW - phylogeny AU - Collyda, C AU - Diplaris, S AU - Mitkas, P AU - Maglaveras, N AU - Pappas, C PY - 2007/// UR - http://view.ncbi.nlm.nih.gov/pubmed/17911914 ER -