Paper Presentation

April 10, 2006

Rui Min

Topic in Bioinformatics, Dr. Charles Yan

- Training HMM structure with genetic algorithm for biological sequence analysis

Overview

• An automatic means of optimizing the structure of HMMs

• Genetic algorithm (GA) for optimizing the HMM structure

• Experiments on two models– Promoter model of C.jejuni

– Coding region model of C.jejuni

• Conclusion

Train HMM structure by GA

Problems• Biologically interpretable structure• Controllable complexity

Method• Combine Baum-Wetch training with

GA, called GA for hidden Markov models (GA-HMM).

Flowchart

Genetic Operations (I)

• Selection– Roulette wheel selection– Stochastic universal sampling

Genetic Operations (II)

• Mutation

Genetic Operations (III)

• Crossover

Selective Baum-Welch

• The Log-likelihood of model k

Fitness value

• Fitness

Experiment I: promoter model of C.jejuni

• Parameters

Structure

Comparison

Experiment II: coding region model of C.jejuni

Experiment II: coding region model of C.jejuni

Conclusion

• Drawbacks– Biologically interpretable structure– No novel types of architecture– No large HMM structures– Those may be the future works

• Merit– Capability of dealing with substructures– GA has an application on bioinformatics

Unstated Aspects

• Too many constant parameters– Probability of population for Baum-Welch

training– Percentage of training/validation– Iteration times– Are they best?

• Unclear parameters– Terminal condition– The distribution of results, t-test?– The specific way to crossover, single?

Questions &

Discussion