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Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

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  • Yong Liu
  • Min Wu
  • Chunyan Miao
  • Peilin Zhao
  • Xiao-Li Li

Abstract

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.Author Summary: This work introduces a computational approach, namely neighborhood regularized logistic matrix factorization (NRLMF), to predicting potential interactions between drugs and targets. The novelty of NRLMF lies in integrating logistic matrix factorization with neighborhood regularization for drug-target interaction prediction. In NRLMF, we model the interaction probability for each drug-target pair using logistic matrix factorization. As the observed interacting drug-target pairs are experimentally verified, they are more trustworthy than the unknown pairs. We propose to assign higher importance levels to interaction pairs and lower importance levels to unknown pairs. In addition, we further improve the prediction accuracy by neighborhood regularization, which considers the neighborhood influences from most similar drugs and most similar targets. To evaluate the performance of NRLMF, we conducted extensive experiments on four benchmark datasets. The experimental results demonstrated that NRLMF usually outperformed five state-of-the-art methods under three different cross-validation settings, in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPR). In addition, we confirmed the practical prediction ability of NRLMF by mapping with the latest version of four online biological databases, including ChEMBL, DrugBank, KEGG, and Matador.

Suggested Citation

  • Yong Liu & Min Wu & Chunyan Miao & Peilin Zhao & Xiao-Li Li, 2016. "Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-26, February.
  • Handle: RePEc:plo:pcbi00:1004760
    DOI: 10.1371/journal.pcbi.1004760
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    References listed on IDEAS

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    1. Kejian Wang & Jiazhi Sun & Shufeng Zhou & Chunling Wan & Shengying Qin & Can Li & Lin He & Lun Yang, 2013. "Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-9, November.
    2. Bin Chen & Ying Ding & David J Wild, 2012. "Assessing Drug Target Association Using Semantic Linked Data," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-10, July.
    3. Twan van Laarhoven & Elena Marchiori, 2013. "Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
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    1. Hansaim Lim & Aleksandar Poleksic & Yuan Yao & Hanghang Tong & Di He & Luke Zhuang & Patrick Meng & Lei Xie, 2016. "Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-26, October.
    2. Anna Cichonska & Balaguru Ravikumar & Elina Parri & Sanna Timonen & Tapio Pahikkala & Antti Airola & Krister Wennerberg & Juho Rousu & Tero Aittokallio, 2017. "Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-28, August.
    3. Qing Ye & Chang-Yu Hsieh & Ziyi Yang & Yu Kang & Jiming Chen & Dongsheng Cao & Shibo He & Tingjun Hou, 2021. "A unified drug–target interaction prediction framework based on knowledge graph and recommendation system," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    4. Benoit Playe & Chloé-Agathe Azencott & Véronique Stoven, 2018. "Efficient multi-task chemogenomics for drug specificity prediction," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-34, October.
    5. Mingxuan Che & Kui Yao & Chao Che & Zhangwei Cao & Fanchen Kong, 2021. "Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism," Future Internet, MDPI, vol. 13(1), pages 1-10, January.

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