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• W2079035862 abstract "Over the last decade, the development of high-throughput techniques has resulted in a rapid accumulation of protein–protein interaction (PPI) data. However, the high-throughput experimental interaction data is prone to exhibit high level of noise. Despite the promising performance of current geometric approaches for increasing the reliability of PPI networks, it is still of major concern to find a better method that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called non-convex semantic embedding (NCSE) for assessing the reliability of interactions. Unlike previous approaches which seek to preserve a predefined distance matrix in the embedding space, NCSE tries to adaptively learn a Euclidean embedding under the simple geometric assumption of PPI networks. We also propose using non-convex cost function in order to improve the robustness of NCSE. The experimental results show that our approach substantially outperforms previous methods on PPI assessment problems. NCSE could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge. The Matlab source code of NCSE is freely available upon request." @default.
• W2079035862 created "2016-06-24" @default.
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• W2079035862 creator A5032061966 @default.
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• W2079035862 date "2013-12-01" @default.
• W2079035862 modified "2023-09-24" @default.
• W2079035862 title "Increasing the reliability of protein–protein interaction networks via non-convex semantic embedding" @default.
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• W2079035862 doi "https://doi.org/10.1016/j.neucom.2013.04.027" @default.
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