Abstract </head> <body text="#000000" bgcolor="#FFFFFF" link="#0000EF" vlink="#CF30CF" alink="#FF0000" nosave> <br> <center><h2> A Pattern Recognition Approach to Infer Genetic Networks from Microarray Data </h2> <br> <h2> Grace S. Shieh </h2> <h3> (Institute of Statistical Science, Academia Sinica) </h3> </center> <br> <p><b>Motivation:</b> Inferring genetic interactions is of interest since it sheds light on important biochemical pathways. From a group of experiments-confirmed genetic interactions, we observed that paired gene expression curves of transcriptional compensatory interactions often were complementary (anti-similar) whereas those of transcriptional diminished interactions looked similar. This motivated us to develop a pattern recognition approach (called PARE) to infer genetic networks from time course microarray gene expression data (MGED). <p><b>Methods:</b> PARE learns paired gene expression patterns from known genetic interactions, either confirmed by biological experiments or from published literature. Specifically, PARE extracts low order characteristics of the nonlinear paired curves, and integrates an optimization algorithm to train the decision score by MGED of known interactions. Subsequently, PARE can predict unknown gene interactions of similar nature. <p><b>Results:</b> Utilizing yeast MGED in Spellman et al. (1998), PARE predicted 112 pairs of genetic interactions and 77 pairs of transcriptional interactions (TIs). Checked against qRT-PCR results and published literatures, respectively, the modified true positive rates are 73% (70%) and 71% (69%) with n-fold (3-fold) cross validation, as compared to 52% and 56% of the latest advance in graphical Gaussian models. The false positive rates of predicting TC and TD interactions for gene pairs formed from yeast genome (3052 synthetic sick or lethal gene pairs) are 5% and 11% (9% and 18%), respectively. <br><br><br> <a href="index.html">Back to main page</a><br><br> <hr> <p><br><br> </body> </html>