Computational Proteins: Predicting Protein Function using Biological Networks

Mark Gerstein

S Balasubramanian, J Karro, N Lan, L Lu, D Lu, N Luscombe, D Milburn,
J Rozowsky, Y Xia, Z Zhang, P Bertone, H Yu, Y Kluger, Y Liu, J Qian,
R Jansen, N Echols, A Paccanaro, V Trifonov, A Edwards, M Snyder, J
Greenblatt, A Emili, N Krogan

MB&B Dept. Yale University

My talk will be concerned with topics in proteomics, in particular
predicting protein function on a genomic scale. We approach this
through the prediction and analysis of biological networks -- both of
protein-protein interactions and transcription-factor-target
relationships. I will describe how these networks can be determined
through Bayesian integration of many genomic features and how they can
be analyzed in terms of various simple topological statistics.

http://bioinfo.mbb.yale.edu
http://topnet.gersteinlab.org


A Bayesian networks approach for predicting protein-protein interactions from
genomic data.
R Jansen, H Yu, D Greenbaum, Y Kluger, NJ Krogan, S Chung, A Emili, M Snyder, JF
Greenblatt, M Gerstein (2003) Science 302: 449-53.

ExpressYourself: A modular platform for processing and visualizing microarray data.
NM Luscombe, TE Royce, P Bertone, N Echols, CE Horak, JT Chang, M Snyder, M
Gerstein (2003) Nucleic Acids Res 31: 3477-82.

TopNet: a tool for comparing biological sub-networks, correlating protein
properties with topological statistics.
H Yu, X Zhu, D Greenbaum, J Karro, M Gerstein (2004) Nucleic Acids Res 32: 328-37.


Genomic analysis of regulatory network dynamics reveals large topological changes.
    NM Luscombe, MM Babu, H Yu, M Snyder, SA Teichmann, M Gerstein (2004)
Nature 431: 308-12.

Annotation transfer between genomes: protein-protein interologs and protein-DNA
regulogs.
    H Yu, NM Luscombe, HX Lu, X Zhu, Y Xia, JD Han, N Bertin, S Chung, M Vidal,
M Gerstein (2004) Genome Res 14: 1107-18.