Bayesian Methods for Inferring Gene Regulatory Networks
Advisor: Adrian Raftery Gene regulatory networks are an important piece in understanding the functioning of living cells. As more and more gene expression data is becoming available, researchers need fast, reliable techniques for inferring these networks. I have developed ScanBMA, a fast Bayesian model averaging algorithm, used to infer networks from time-series data. I have also developed Model-based Clustering with Data Correction (MCDC), a method for automatically detecting and correcting errors that systematically affect some but not all data. I have applied MCDC to data from the NIH LINCS L1000 project and shown that it does lead to improvement in subsequent analysis.