Discovery of biological networks from diverse functional genomic data.
We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web.
Myers
Chad L
CL
Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ 08544, USA. clmyers@princeton.edu
Robson
Drew
D
Wible
Adam
A
Hibbs
Matthew A
MA
Chiriac
Camelia
C
Theesfeld
Chandra L
CL
Dolinski
Kara
K
Troyanskaya
Olga G
OG
D001499:Bayes Theorem; D018797:Cell Cycle Proteins / Q000378:metabolism; D019295:Computational Biology; D003196:Computer Graphics; D023281:Genomics; D018841:HSP90 Heat-Shock Proteins / Q000378:metabolism; D018832:Molecular Chaperones / Q000378:metabolism; D025941:Protein Interaction Mapping; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics* / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism
C513270:CDC37 protein, S cerevisiae; D018797:Cell Cycle Proteins; D018841:HSP90 Heat-Shock Proteins; D018832:Molecular Chaperones; D029701:Saccharomyces cerevisiae Proteins