ACM Transactions on Mathematical Software (TOMS)
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
The Hyperbell Algorithm for Global Optimization: A Random Walk Using Cauchy Densities
Journal of Global Optimization
HIDDEN MARKOV MODELS IN COMPUTATIONAL BIOLOGY: APPLICATIONS TO PROTEIN MODELING
HIDDEN MARKOV MODELS IN COMPUTATIONAL BIOLOGY: APPLICATIONS TO PROTEIN MODELING
Global optimization for constrained nonlinear programming (asymptotic convergence)
Global optimization for constrained nonlinear programming (asymptotic convergence)
A Hidden Markov Model for Transcriptional Regulation in Single Cells
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
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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.