A probabilistic graph-theoretic approach to integrate multiple predictions for the protein-protein subnetwork prediction challenge.
The protein-protein subnetwork prediction challenge presented at the 2nd Dialogue for Reverse Engineering Assessments and Methods (DREAM2) conference is an important computational problem essential to proteomic research. Given a set of proteins from the Saccharomyces cerevisiae (baker's yeast) genome, the task is to rank all possible interactions between the proteins from the most likely to the least likely. To tackle this task, we adopt a graph-based strategy to combine multiple sources of biological data and computational predictions. Using training and testing sets extracted from existing yeast protein-protein interactions, we evaluate our method and show that it can produce better predictions than any of the individual data sources. This technique is then used to produce our entry for the protein-protein subnetwork prediction challenge.
Chua
Hon Nian
HN
Data Mining Department, Institute for Infocomm Research, Singapore. hnchua@i2r.a-star.edu.sg
Hugo
Willy
W
Liu
Guimei
G
Li
Xiaoli
X
Wong
Limsoon
L
Ng
See-Kiong
SK
D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D016681:Genome, Fungal; D008957:Models, Genetic; D025941:Protein Interaction Mapping; D012372:ROC Curve; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism
D029701:Saccharomyces cerevisiae Proteins