1. Inferring Gene Regulatory Networks from Asynchronous Microarray Data David Oviatt, Dr. Mark Clement, Dr. Quinn Snell, Kenneth Sundberg Department of Computer Science, Brigham Young University, Provo, UT Jared Allen, Dr. Randall Roper Department of Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN

2. Purpose: Use microarray data to infer the gene regulatory network of an organism

3. Other Methods' Unreasonable Requirements High number of samples Time series data

4. Problems: Scarcity of microarray data Large size of networks Noise

5. AIRnet: Asynchronous Inference of Regulatory networks Classify gene levels using k-means clustering Compute influence vectors (i.v.) Convert i.v.'s into a sorted list of edges Use Kruskal's algorithm to find the minimum-cost spanning tree

6. Influence Vectors Perform pairwise- comparisons of change in gene levels between samples, adding or subtracting from i.v. Divide i.v. by the total number of comparisons

7. In-silico Data DREAM3 competition - http://wiki.c2b2.columbia.edu/dream/index.php/The_DREA M3_Challenges Laboratory of Intelligent Systems: Thomas Schaffter and Daniel Marbach - GeneNet Weaver - http://lis.epfl.ch/

8. Clockwise from top left: simulated E.coli 1 network; E.coli 1 inferred correlations above 50%; simulated E.coli 2 network; E.coli 2 inferred correlations above 50%; inferred networks made using 2 bins for each gene.

9. Metrics Precision Recall F-score Accuracy Sensitivity Specificity MCC – Matthews Correlation Coefficient

10. AIRnet Compared to Random 1000 random predictions created for each test case Mean score of each metric reported for each network size

11. Score Summaries:

12. The End