Paper Info
Title
Simulated annealing algorithm with biased neighborhood distribution for training profile models
Abstract
Functional biological sequences, which typically come in families, have retained some level of similarity and function during evolution. Finding consensus regions, alignment of sequences, and identifying the relationship between a sequence and a family allow inferences about the function of the sequences. Profile hidden Markov models (HMMs) are generally used to identify those relationships. A profile HMM can be trained on unaligned members of the family using conventional algorithms such as Baum-Welch, Viterbi, and their modifications. The overall quality of the alignment depends on the quality of the trained model. Unfortunately, the conventional training algorithms converge to suboptimal models most of the time. This work proposes a training algorithm that early identifies many imperfect models. The method is based on the Simulated Annealing approach widely used in discrete optimization problems. The training algorithm is implemented as a component in HMMER. The performance of the algorithm is discussed on protein sequence data.
Year
DOI
Venue
2006
10.1007/11875604_71
ISMIS
Keywords
Field
DocType
neighborhood distribution,overall quality,simulated annealing approach,conventional training algorithm,simulated annealing algorithm,functional biological sequence,profile hmm,markov model,conventional algorithm,training algorithm,protein sequence data,training profile model,trained model,baum welch,hidden markov model,simulated annealing,discrete optimization,protein sequence
Sequence alignment,Simulated annealing,Similitude,Pattern recognition,Inference,Markov model,Computer science,Artificial intelligence,Multiple sequence alignment,Hidden Markov model,Machine learning,Viterbi algorithm
Conference
Volume
ISSN
ISBN
4203
0302-9743
3-540-45764-X
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Anton Bezuglov111.19
Juan E. Vargas262.46