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Presenter : YAN-SHOU SIE Authors : Christos Ferles ∗, Andreas Stafylopatis 2013. NN

Self-Organizing Hidden Markov Model Map (SOHMMM). Presenter : YAN-SHOU SIE Authors : Christos Ferles ∗, Andreas Stafylopatis 2013. NN. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : YAN-SHOU SIE Authors : Christos Ferles ∗, Andreas Stafylopatis 2013. NN

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  1. Self-Organizing Hidden Markov Model Map (SOHMMM) Presenter : YAN-SHOU SIE Authors : Christos Ferles∗, Andreas Stafylopatis2013. NN

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • The advent of efficient experimental technologies has led to an exponential growth of linear descriptions of protein, DNA and RNA chain molecules requiring automated analysis. • Therefore, the need for computational /statistical / machine learning algorithms and techniques, for the qualitative and quantitative description of biological molecules, is today stronger than ever.

  4. Objectives • Here proposed a SOHMMM model to help analyze the DNA/protein sequences. • SOHMMM is an integration of the SOM and the HMM principles.

  5. Methodology • Hidden Markov Model(HMM)

  6. Methodology • Hidden Markov Model(HMM) • Hidden Markov model

  7. Methodology • Hidden Markov Model(HMM) • Estimating model parameters

  8. Methodology • SOHMMM • Generic framework

  9. Methodology • SOHMMM • Analysis of the SOHMMM

  10. Methodology • SOHMMM • Analysis of the SOHMMM

  11. Methodology • SOHMMM • The SOHMMM learning algorithm Forward-backward Algorithm

  12. Experiments • Artificial sequence data

  13. Experiments • Splice junction gene sequences

  14. Experiments • Splice junction gene sequences

  15. Experiments • Splice junction gene sequences

  16. Experiments • Splice junction gene sequences

  17. Conclusions • SOHMMM can provide useful automated analysis and visualization capabilities help analyze DNA Chain. • Compare other method have a lower error rate and better analyze result.

  18. Comments • Advantages • For the analysis of biological information is very helpful. • Applications • bioinformaticsetwork forensics

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