paper presentation april 10, 2006 rui min topic in bioinformatics, dr. charles yan - training hmm...
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![Page 1: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/1.jpg)
Paper Presentation
April 10, 2006
Rui Min
Topic in Bioinformatics, Dr. Charles Yan
- Training HMM structure with genetic algorithm for biological sequence analysis
![Page 2: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/2.jpg)
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
![Page 3: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/3.jpg)
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).
![Page 4: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/4.jpg)
Flowchart
![Page 5: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/5.jpg)
Genetic Operations (I)
• Selection– Roulette wheel selection– Stochastic universal sampling
![Page 6: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/6.jpg)
Genetic Operations (II)
• Mutation
![Page 7: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/7.jpg)
Genetic Operations (III)
• Crossover
![Page 8: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/8.jpg)
Selective Baum-Welch
• The Log-likelihood of model k
![Page 9: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/9.jpg)
Fitness value
• Fitness
![Page 10: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/10.jpg)
Experiment I: promoter model of C.jejuni
• Parameters
![Page 11: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/11.jpg)
Structure
![Page 12: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/12.jpg)
Comparison
![Page 13: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/13.jpg)
Experiment II: coding region model of C.jejuni
![Page 14: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/14.jpg)
Experiment II: coding region model of C.jejuni
![Page 15: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/15.jpg)
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
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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?
![Page 17: Paper Presentation April 10, 2006 Rui Min Topic in Bioinformatics, Dr. Charles Yan - Training HMM structure with genetic algorithm for biological sequence](https://reader031.vdocument.in/reader031/viewer/2022032522/56649d625503460f94a447ea/html5/thumbnails/17.jpg)
Questions &
Discussion