Evolving the structure of hidden Markov Models

TY - JOUR

T1 - Evolving the structure of hidden Markov Models

AU - won, K. J.

AU - Prugel-Bennett, A.

AU - Krogh, A.

PY - 2006

Y1 - 2006

N2 - A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature.

AB - A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature.

U2 - 10.1109/TEVC.2005.851271

DO - 10.1109/TEVC.2005.851271

M3 - Journal article

SN - 1089-778X

VL - 10

SP - 39

EP - 49

JO - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

IS - 1

ER -