News & Events
- 04/06/2011
Lab's research highlighted in U of M bulletin - 02/16/2011
Genome Biology publishes Bringing order to protein disorder through comparative genomics and genetic interactions. - 12/12/2010
PLoS Computational Biology publishes A scalable approach for discovering conserved active subnetworks across species. - 11/16/2010
Molecular Systems Biology publishes Genetic interactions reveal the evolutionary trajectories of duplicate genes. - 11/14/2010
Nature Methods publishes Quantitative analysis of fitness and genetic interactions in yeast on a genome scale
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.
