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A Probabilistic Graph‐Theoretic Approach to Integrate Multiple Predictions for the Protein–Protein Subnetwork Prediction Challenge
Author(s) -
Chua Hon Nian,
Hugo Willy,
Liu Guimei,
Li Xiaoli,
Wong Limsoon,
Ng SeeKiong
Publication year - 2009
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2008.03760.x
Subject(s) - subnetwork , probabilistic logic , computer science , graph , computational biology , machine learning , theoretical computer science , artificial intelligence , biology , computer network
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.