While we now know the sequence of many organisms (including mice and humans) we still know relatively little about the roles played by the genes and proteins encoded within these sequences. Researchers have generated a great deal of data that can potentially shed light on to the functions and processes performed by proteins, however, these datasets are generally noisy, heterogeneous, and very large. Our group develops and applies machine learning and data mining techniques to these data that overcome these challenges in order to form highly confident predictions of protein roles. We then take these predictions back to the lab bench with our collaborators to confirm their validity.
- Hess DC, Myers CL, Huttenhower C, Hibbs MA, Hayes AP, Paw J, Clore JJ, Mendoza RM, Luis B, Nislow C, Giaever G, Costanzo M, Troyanskaya OG, Caudy AA. Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis. PLoS Genetics, 2009
- Hibbs MA, Myers CL, Huttenhower C, Hess DC, Li K, Caudy AA, Troyanskaya OG. Directing experimental biology: a case study in mitochondrial biogenesis. PLoS Computational Biology, 2009
- Myers CL, Robson D, Wible A, Hibbs MA, Chiriac C, Theesfeld CL, Dolinski K, Troyanskaya OG. Discovery of biological networks from diverse functional genomic data. Genome Biology, 2005
- Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics, 2007
- Guan Y, Ackert-Bicknell CL, Kell B, Troyanskaya OG, Hibbs MA. Functional genomics complements quantitative genetics in identifying disease-gene associations. PLoS Computational Biology, 2010