Abstract
We propose here a multi-label semi-supervised learning algorithm, PfunBG, to predict protein functions, employing a bi-relational graph (BG) of proteins and function annotations. Different from most, if not all, existing methods that only consider the partially labeled protein-protein interaction (PPI) network, the BG comprises three components, a PPI network, a function class graph induced from function annotations of such proteins, and a bipartite graph induced from function assignments. By referring to proteins and function classes equally as vertices, we exploit network propagation to measure how closely a specific function class is related to a protein of interest. The experiments on a yeast PPI network illustrate its effectiveness and efficiency.
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Jiang, J.Q. (2011). Learning Protein Functions from Bi-relational Graph of Proteins and Function Annotations. In: Przytycka, T.M., Sagot, MF. (eds) Algorithms in Bioinformatics. WABI 2011. Lecture Notes in Computer Science(), vol 6833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23038-7_12
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DOI: https://doi.org/10.1007/978-3-642-23038-7_12
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