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

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bacteria and eukaryotes

Hidden Markov Models (HMM)

High-throughput Biological Data The data deluge

KEY CONCEPT Entire genomes are sequenced, studied, and compared.

KEY CONCEPT Entire genomes are sequenced, studied, and compared.

KEY CONCEPT Entire genomes are sequenced, studied, and compared.

KEY CONCEPT Entire genomes are sequenced, studied, and compared.

KEY CONCEPT Entire genomes are sequenced, studied, and compared.

Presentation transcript:

Intelligent Database Systems Lab Presenter : YAN-SHOU SIE Authors : Christos Ferles ∗, Andreas Stafylopatis NN Self-Organizing Hidden Markov Model Map (SOHMMM)

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab Objectives Here proposed a SOHMMM model to help analyze the DNA/protein sequences. SOHMMM is an integration of the SOM and the HMM principles.

Intelligent Database Systems Lab Methodology Hidden Markov Model(HMM)

Intelligent Database Systems Lab Methodology Hidden Markov Model(HMM) – Hidden Markov model

Intelligent Database Systems Lab Methodology Hidden Markov Model(HMM) – Estimating model parameters

Intelligent Database Systems Lab Methodology SOHMMM – Generic framework

Intelligent Database Systems Lab Methodology SOHMMM – Analysis of the SOHMMM

Intelligent Database Systems Lab Methodology SOHMMM – Analysis of the SOHMMM

Intelligent Database Systems Lab Methodology SOHMMM – The SOHMMM learning algorithm Forward-backward Algorithm

Intelligent Database Systems Lab Experiments Artificial sequence data

Intelligent Database Systems Lab Experiments Splice junction gene sequences

Intelligent Database Systems Lab Experiments Splice junction gene sequences

Intelligent Database Systems Lab Experiments Splice junction gene sequences

Intelligent Database Systems Lab Experiments Splice junction gene sequences

Intelligent Database Systems Lab 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.

Intelligent Database Systems Lab Comments Advantages – For the analysis of biological information is very helpful. Applications – bioinformaticsetwork forensics