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

2. 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

3. 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).

4. Flowchart

5. Genetic Operations (I) Selection –Roulette wheel selection –Stochastic universal sampling

6. Genetic Operations (II) Mutation

7. Genetic Operations (III) Crossover

8. Selective Baum-Welch The Log-likelihood of model k

9. Fitness value Fitness

10. Experiment I: promoter model of C.jejuni Parameters

11. Structure

12. Comparison

13. Experiment II: coding region model of C.jejuni

15. 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

16. 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?

17. Questions & Discussion