Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
Readings in uncertain reasoning
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Fast protein classification with multiple networks
Bioinformatics
GRAPPIN: Bipartite GRAph Based Protein-Protein Interaction Network Similarity Search
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
Pairwise global alignment of protein interaction networks by matching neighborhood topology
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Protein function prediction based on patterns in biological networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
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Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of assigning functional annotations to proteins in a high-throughput manner. In this article, the authors provide an introduction to the task of automated protein function prediction. They discuss about the motivation for automated protein function prediction, the challenges faced in this task, as well as some approaches that are currently available. In particular, they take a closer look at methods that use protein-protein interaction for protein function prediction, elaborating on their underlying techniques and assumptions, as well as their strengths and limitations.