A Topological Measurement for Weighted Protein Interaction Network
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label feature transform for image classifications
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Multi-label linear discriminant analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Image annotation using bi-relational graph of images and semantic labels
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Simultaneous image classification and annotation via biased random walk on tri-relational graph
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.