Recent developments in biotechnology have enabled quantitative measurement of diverse cellular phenomena. For instance, microarray technology allows biologists to measure the expression of all genes in the genome on a single chip. Other technology allows high-throughput measurement of physical interactions between proteins, which are an important mechanism behind most cellular processes. These recent developments have generated an unprecedented amount of data for several different organisms. These data promise to revolutionize our understanding of biology, but integrating information across several noisy, heterogeneous datasets to derive holistic models of the cell requires sophisticated computational approaches.

Our research focuses on machine learning approaches for integrating diverse genomic data to make inferences about biological networks. The main purpose of our work is to further our understanding of gene function and how genes or proteins interact to carry out cellular processes.

(a)                                      (b)

Yeast (Saccharomyces cerevisiae) with flourescently tagged, mitochondria-localized protein.  (a) is normal, wild-type yeast, and (b) is a mutant where a predicted mitochondria gene has been deleted.  The mitochondria have abnormal morphology, suggesting we correctly predicted mitochondrial function.