Abstract
Recent developments in the high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. To predict PPI in a net-work, we develop an intrinsic geometry structure (IGS) for the network, which exploits the intrinsic and hidden relationship among proteins in the network through a heat diffusion process. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods.
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Fang, Y., Sun, M., Dai, G., Ramani, K. (2014). The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_56
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DOI: https://doi.org/10.1007/978-3-319-09330-7_56
Publisher Name: Springer, Cham
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