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Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,*,A New Dynamic Bayesian Network(DBN)Approach for Identifying Gene Regulatory Networks from Time Course Microarray Data,Jim Vallandingham,By Min Zou and Suzanne Conzen,A New Dynamic Bayesian Network,1,Dynamic Bayesian Networks(DBN),For modeling time-series data,Such as microarray data,capture the fact that time flows forward,Interested in how genes regulate each other over time,Dynamic Bayesian Networks(DBN,2,Dynamic Bayesian Networks(DBN),Dynamic Bayesian Networks(DBN,3,Dynamic Bayesian Networks(DBN),Time,Dynamic Bayesian Networks(DBN,4,Problems with DBNs,Lack a way to determine biologically relevant transcriptional time lag,Current methods assume same time lag for all potential regulator-target pairs,Results in low accuracy of predicting gene relationships,Excessive computational cost,Prevents use of DBNs with large scale datasets,Problems with DBNsLack a way,5,New DBN Method Improvements,Determine biologically relevant transcriptional time lag,Look at initial regulation of regulator and potential target to determine time lag,Analyzed for each relationship,Will improve relation predictions,Reduce computational cost,Only consider genes(up/down)regulated before or at the same time as potential target,Reduces search space,Reduces cost,New DBN Method ImprovementsDet,6,General Outline of Method,General Outline of Method,7,Hypothetical Example,Used to illustrate novel DBN approach,4 hypothetical genes:A D,6 time points T,1,T,6,Evenly spaced:indicative of actual data sets.,Process broken into 3 major steps,Hypothetical ExampleUsed to il,8,Hypothetical Example,Step 1:Selection of potential regulators for each gene,First,determine time points of changes in expression for each gene,Used Thresholds to determine when regulation has occurred,1.2 fold,up-regulation,0.7 fold,down-regulation,Find Potential Regulators,Pick only genes with earlier or simultaneous changes as regulator candidates,Used to reduce number of nodes considered,Hypothetical ExampleStep 1:S,9,Hypothetical Example,Initial up-regulation at T,4,Up-regulation threshold,Down-regulation threshold,Dynamic Expression profile for Gene D,Hypothetical ExampleInitial up,10,Hypothetical Example,Dynamic Expression profiles for,Genes A&D,Possible regulator of D,Hypothetical ExampleDynamic Ex,11,Hypothetical Example,Hypothetical Example,12,Hypothetical Example,Step 2:Estimation of biologically relevant transcriptional time lag,Time between expression changes of potential regulator and target genes represents a biologically relevant time period,Can vary from 0(simultaneous)to many steps,Using this time period should result in an increase of correct relationships,Hypothetical ExampleStep 2:E,13,Hypothetical Example,Step 2,cont:,Looking at D as target gene and A-C as potential regulators,A:2 time units,B:2 time units,C:1 time unit,Group potential regulators based on time lag,A,&,B,C,Hypothetical ExampleStep 2,co,14,Hypothetical Example,t=two time units,Hypothetical Examplet=two ti,15,Hypothetical Example,Hypothetical Example,16,Hypothetical Example,Step 3:Gene regulatory network modeling,Use DBN to predict gene regulatory network,For DBN variables:,Use,2,if expression level is equal to or higher than average expression level over all time points,Use,1,if expression level is lower than average level,Focus of DBN is to predict correlation,not expression value for any given point,Hypothetical ExampleStep 3:G,17,Hypothetical Example,Step 3,cont:,Generate subgroups of groups of potential regulators based on user defined minimum and maximum regulators,For Hypothetical Example:,Subsets for group A&B,A,B,A,B,Assuming maximum 2,minimum 1,Subset for group C C,Hypothetical ExampleStep 3,co,18,Hypothetical Example,Step 3,cont:,For each subset,using transcriptional time lag from step 2 to organize expression data into NxM matrix,N:number of potential regulators+target,T:number of time points from original sampling,t:estimated transcriptional time lag,From step 2,M:number of time points in the data matrix=,T t,Hypothetical ExampleStep 3,co,19,Hypothetical Example,Step 3,cont:,Expression value of potential regulators at time Tn are aligned with expression value of the target gene at time Tn+t,t may vary for the different expression data matrices,Hypothetical ExampleStep 3,co,20,Hypothetical Example,1=below average,2=at or above average,Hypothetical Example1=below,21,Hypothetical Example,Step 3,cont:,Matrix used to find conditional probabilities of the expression the of target gene in relation to its potential regulator gene(s),Pick subset of potential regulator(s)with highest log marginal likelihood score as the estimated regulator(s),Hypothetical ExampleStep 3,co,22,Hypothetical Example,Conditional probabilities,Target,D,Regulator,A,Gene,T1,T2,T3,T4,A,1,2,2,2,D,1,2,2,1,Discrete Expression Values,Hypothetical ExampleConditiona,23,Biological Experimentation,Comparison
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