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Open AccessArticle

Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein–Protein Interaction Networks

by Suyu Mei 1,* and Kun Zhang 2,*
1
Software College, Shenyang Normal University, Shenyang 110034, China
2
Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(20), 5075; https://doi.org/10.3390/ijms20205075 (registering DOI)
Received: 27 September 2019 / Accepted: 11 October 2019 / Published: 12 October 2019
(This article belongs to the Section Molecular Informatics)
Rapid reconstruction of genome-scale protein–protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not interact) is still a major issue that needs to be properly addressed in computational modeling. In this study, we take advantage of the very limited experimentally verified negative data from Negatome to infer more negative data for computational modeling. We assume that the paralogs or orthologs of two non-interacting proteins also do not interact with high probability. We coin an assumption as “Neglog” this assumption is to some extent supported by paralogous/orthologous structure conservation. To reduce the risk of bias toward the negative data from Negatome, we combine Neglog with less biased random sampling according to a certain ratio to construct training data. L2-regularized logistic regression is used as the base classifier to counteract noise and train on a large dataset. Computational results show that the proposed Neglog method outperforms pure random sampling method with sound biological interpretability. In addition, we find that independent test on negative data is indispensable for bias control, which is usually neglected by existing studies. Lastly, we use the Neglog method to validate the PPIs in STRING, which are supported by gene ontology (GO) enrichment analyses. View Full-Text
Keywords: protein–protein interaction; paralog/ortholog; negative data sampling; machine learning; l2-regularized logistic regression protein–protein interaction; paralog/ortholog; negative data sampling; machine learning; l2-regularized logistic regression
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MDPI and ACS Style

Mei, S.; Zhang, K. Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein–Protein Interaction Networks. Int. J. Mol. Sci. 2019, 20, 5075.

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