Abstract
Many previous computational methods for protein function prediction make prediction one function at a time, fundamentally, which is equivalent to assume the functional categories of proteins to be isolated. However, biological processes are highly correlated and usually intertwined together to happen at the same time, therefore it would be beneficial to consider protein function prediction as one indivisible task and treat all the functional categories as an integral and correlated prediction target. By leveraging the function-function correlations, it is expected to achieve improved overall predictive accuracy. To this end, we develop a novel network based protein function prediction approach, under the framework of multi-label classification in machine learning, to utilize the function-function correlations. Besides formulating the function-function correlations in the optimization objective explicitly, we also exploit them as part of the pairwise protein-protein similarities implicitly. The algorithm is built upon the Green’s function over a graph, which not only employs the global topology of a network but also captures its local structural information. We evaluate the proposed approach on Saccharomyces cerevisiae species. The encouraging experimental results demonstrate the effectiveness of the proposed method.
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Wang, H., Huang, H., Ding, C. (2012). Function-Function Correlated Multi-Label Protein Function Prediction over Interaction Networks. In: Chor, B. (eds) Research in Computational Molecular Biology. RECOMB 2012. Lecture Notes in Computer Science(), vol 7262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29627-7_32
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DOI: https://doi.org/10.1007/978-3-642-29627-7_32
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