News & Events
- 04/30/2013
PLoS Computational Biology publishes Distinct Types of Disorder in the Human Proteome: Functional Implications for Alternative Splicing. - 03/29/2013
Molecular Cancer Therapeutics publishes Profiling bortezomib resistance identifies secondary therapies in a mouse myeloma model - 02/21/2013
Check out our new GitHub page - 01/05/2013
Outreach Activity Workshop for NSF Maize Ionomics. Visit here for more details. - 07/18/2012
Genome Biology Publishes Conserved rules govern genetic interaction degree across species. - 07/17/2012
PNAS publishes Reshaping of the maize transcriptome by domestication. - 04/06/2011
Lab's research highlighted in U of M bulletin
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.
