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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling. Presented by: Norhaini Binti Baba Supervisor: Dr. Mohd Saberi Bin Mohamad. Literature review. Research Methodology. Materials & Methods. Outline. Introduction. Conclusion.

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Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

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  1. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling Presented by: NorhainiBinti Baba Supervisor: Dr. MohdSaberi Bin Mohamad

  2. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  3. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion INTRODUCTION Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  4. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Gene regulatory network • Collection of DNA segment • Plays role in governing the rates at which gene are transcribed into mRNA to protein. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  5. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Continuous Dynamic Bayesian Network • Directed graphical models of stochastic process • Contain nodes gene, edges interaction between genes Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  6. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Problem background Parametric model provide limited information on GRNs (TarmoAijoet al ., 2009) Difficult of handling cyclic network of gene regulation (Sunyong Kim et al., 2004) Discretisation might cause information loss (SunyongKim et al., 2003) Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  7. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Objectives To study and understand the framework of the continuous Dynamic Bayesian Networks. To model the continuous Dynamic Bayesian Network for the construction of gene regulatory networks using the Bayes Net Toolbox. To construct gene regulatory networks and evaluate the performance based on implementation of continuous Dynamic Bayesian Networks. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  8. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Scope Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  9. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Justification Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  10. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion LITERATURE REVIEW Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  11. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Gene regulation • Process of cell or viruses regulate gene into gene product • Occur at 4 level in eukaryotes. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  12. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Gene Regulatory Network • Genes  nodes. • The interaction between any pair of genes edges. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  13. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Microarray technology • To monitor and quantify changes in gene expression. • Lead to revolution in speed and scope of genetic analysis of regulatory networks. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  14. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Gene Expression Data • Structure of how data is organized after hybridization of DNA microarray. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  15. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Comparison of Previous Methods Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  16. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion RESEARCH METHODOLOGY Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  17. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Research framework Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  18. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Data and resource collection Imputation missing value Model cDBN using BNT Visualize DAG (graphviz) Compare with KEGG database Find optimal network give best performance Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  19. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Data sources • Saccharomycescerevisiae Dataset Spellman et al., 1988 • Escherichia coli Dataset DNA Microarray Datasets website Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  20. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion MATERIALS & METHODS Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  21. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  22. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Imputation missing value • Prepare a complete dataset for constructing dynamic Bayesian networks. • Missing values must be imputed. • Using knnimpute >> A = data; >> ecoli = knnimpute (A) Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  23. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Learning Algorithm to model cDBN in BNT Parameter learning Structure learning • Parameter estimation for continuous variables. • Maximum Likelihood estimation. • Data  CPTs • Constrained-based : test independencies • Search-and-score: define selection of criterion that measures goodness of a model based on scoring function. • Select the highest scoring model. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  24. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Construct gene regulatory network • Implement the cDBN model in BNT using Saccharomycescerevisiae and Escherichia coli datasets. Gene regulatory constructed is displayed in DAG using GraphViz. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  25. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion CONCLUSION Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  26. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Contributions cDBN - to construct the gene regulatory network Overcome the problem of discretisation Identify the best performance of gene regulatory network Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  27. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Future works Model the continuous Dynamic Bayesian Network using the Bayes Net Toolbox Construct gene regulatory network and visualize the DAG using GraphViz Compare gene network with KEGG database to evaluate the performance Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  28. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Conclusion • Continuous dynamic bayesian network constructed systematically and criteria for learning network structures for continuous data. • Advantage of using continuous dynamic Bayesian network is discretisation is not required. • Helps to study interaction of gene regulation. Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  29. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion Q & A Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

  30. Literature review Research Methodology Materials & Methods Outline Introduction Conclusion THANK YOU Continuous Dynamic Bayesian Network For Gene Regulatory Network Modeling

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