News & Events
Genome Biology publishes Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection doi:10.1186/gb-2014-15-4-r64
Roman Briskine sucessfully defends his PhD thesis titled Computational approaches for analyzing variation in the transcriptome and
methylome of Zea mays
Yungil Kim sucessfully defends his PhD thesis titled Reverse engineering biological networks: computational approaches for modeling biological systems from perturbation data
Raamesh Deshpande successfully defends his PhD thesis titled Computational methods to explore chemical and genetic interaction networks for novel human therapies.
PLoS Computational Biology publishes Distinct Types of Disorder in the Human Proteome: Functional Implications for Alternative Splicing.
Molecular Cancer Therapeutics publishes Profiling bortezomib resistance identifies secondary therapies in a mouse myeloma model
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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.