Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence.

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Presentation transcript:

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

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