Presentation is loading. Please wait.

Presentation is loading. Please wait.

Variable selection method for Boolean networks 2005. 08. 11 Ha Seong, Kim Bioinformatics & Biostatistics Lab., SNU.

Similar presentations


Presentation on theme: "Variable selection method for Boolean networks 2005. 08. 11 Ha Seong, Kim Bioinformatics & Biostatistics Lab., SNU."— Presentation transcript:

1 Variable selection method for Boolean networks Ha Seong, Kim Bioinformatics & Biostatistics Lab., SNU

2 Table of contents Introduction Objective Boolean networks Drawback Method GLM for binary data Result Inference of gene regulatory networks Computing time Discussion

3 INTRODUCTION

4 Objective Introduce a variable selection method to reduce the computing time in the Boolean network construction.

5 Boolean networks Gene 1Gene 2…Gene n time …1.143 time …0.648 …………… time m …0.532 Gene 1Gene 2…Gene n time 100…1 time 201…0 …………… time m10…0 Binary data Time series microarray data Find Boolean Functions REVEL algorithm Identification problem Consistency problem Best-Fit Extension problem G1 G2 G5 G4G3 G6 f 3 = G1 and G2 f 5 = G6 f 4 = G3 and not G5 Boolean functions Network structure

6 Drawback of Boolean networks G1G2G3G4G5G6 time time time time time Boolean Functions for G4 Truth table ErrorG4f 4,1 G1G f 4,1 G1andG2 f 4,2 G1andnotG2 f 4,3 G1orG2 f 4,4 G1ornotG2 f 4,5 notG1andG2 f 4,6 notG1andnotG2 f 4,7 notG1orG2 f 4,8 notG1ornotG2 k : indegree, n : total genes, m : total time points Three Boolean operator (AND, OR, NOT) LAHDESMAKI H Binary data Time complexity Find Boolean Functions (indegree k=2)

7 METHOD

8 GLM for binary data G1G2G3G4G5G6 time time time time time Binary data 1. Simple regression 2. GLM Y,beta~normal t-test, p-value

9 2x2 Contingency Table G4 G G2G3G4G5G6 time time time time time time time time time time Binary data G4 G G4 G Independence test f4= G3 and not G5

10 RESULT

11 Simmulated network G1 G5 G8 G6 G4 G3 G2 G7 G1 G2 G3 G4 G5 G6 G7 G G1 G2 G3 G4 G5 G6 G7 G G1 G2 G3 G4 G5 G6 G7 G G1 G2 G3 G4 G5 G6 G7 G experiments with different initial state 8 genes 10 time points No noise Time Gene

12 f1 = not G8 Number of Bf:1, error: f2 = G1 Number of Bf:1, error: f3 = not G1 Number of Bf:1, error: f4 = G3 Number of Bf:1, error: f5 = G3 and G4 Number of Bf:1, error: f6 = not G2 and G5 Number of Bf:1, error: f7 = G6 Number of Bf:1, error: f8 = G7 Number of Bf:1, error: elapsed time is sec Result of original Boolean networks G1 G5 G8 G6 G4 G3 G2 G7

13 Result of variable selection method. Standard Chi- Par DF Estimate Error Square Pr > ChiSq x x x x x x x x (Log Likelihood E308) x x x x x x x x x x x x x x x x x x <.0001 x x x x x x Standard Chi- Par DF Estimate Error Square Pr > ChiSq x x <.0001 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x G1 G2 G3 G4 G5 G6 G7 G8

14 Computing time Boolean network with variable selection methodOriginal Boolean network method sec 548 sec Yeast cell cycle (spellman 1998) Faster 90 times than original Boolean networks k=4 k=3

15 DISCUSSION

16 Modify the method Add a real data analysis


Download ppt "Variable selection method for Boolean networks 2005. 08. 11 Ha Seong, Kim Bioinformatics & Biostatistics Lab., SNU."

Similar presentations


Ads by Google