Computational biology focuses on transforming genomics data into testable hypotheses. As experimental biologists become adept at generating data using high-throughput methods, computational scientists play an important role in analyzing, interpreting, and integrating these data. Scientific initiatives at GNF often utilize multiple technology platforms to investigate a single biological question. Data mining efforts are often based on a diverse collection of data types, including gene expression, genome sequence, genotype, and functional genomics. Methods such as statistics and machine learning are used to analyze these data and identify new candidate drug targets or to elucidate new biological mechanisms. Current and past research initiatives have spanned mouse genetics, cancer biology, and transcriptional regulation.
Selected Publications
- Wu C, Delano DL, Mitro N, Su SV, Janes J, McClurg P, Batalov S, Welch GL, Zhang J, Orth AP, Walker JR, Glynne RJ, Cooke MP, Takahashi JS, Shimomura K, Kohsaka A, Bass J, Saez E, Wiltshire T, Su AI. PLoS Genet 2008;4(5):e1000070.
- Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. <em>Proc Natl Acad Sci U S A</em> 2004;101(16):6062-7.
