Intelligent Database Systems Lab Presenter : YAN-SHOU SIE Authors : Christos Ferles ∗, Andreas Stafylopatis 2013. NN Self-Organizing Hidden Markov Model

Intelligent Database Systems Lab

Presenter : YAN-SHOU SIE

Authors : Christos Ferles , Andreas Stafylopatis∗

2013. NN

Self-Organizing Hidden Markov Model Map (SOHMMM)


Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments


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.


Objectives

• Here proposed a SOHMMM model to help analyze the DNA/protein sequences.

• SOHMMM is an integration of the SOM and the HMM principles.


Methodology• Hidden Markov Model(HMM)


Methodology• Hidden Markov Model(HMM)–Hidden Markov model


Methodology• Hidden Markov Model(HMM)– Estimating model parameters


Methodology• SOHMMM– Generic framework


Methodology• SOHMMM– Analysis of the SOHMMM


Methodology• SOHMMM– Analysis of the SOHMMM


Methodology• SOHMMM– The SOHMMM learning algorithm

Forward-backward Algorithm


Experiments• Artificial sequence data


Experiments• Splice junction gene sequences








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.


Comments• Advantages– For the analysis of biological information is very

helpful.• Applications– bioinformaticsetwork forensics

Documents

Intelligent Database Systems Lab Presenter : YAN-SHOU SIE Authors : Christos Ferles ∗, Andreas Stafylopatis 2013. NN Self-Organizing Hidden Markov Model