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Regular Paper

Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

School of Computer Science and Engineering, Central South University, Changsha 410083, China
Hunan Provincial Key Laboratory of Bioinformatics, Central South University, Changsha 410083, China
School of Science, Shaoyang University, Shaoyang 422000, China
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Abstract

Identifying the potential drug-target interactions (DTI) is critical in drug discovery. The drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction performance. However, many corresponding models cannot accurately express the relationship between similarity features and DTI features. In order to rationally represent the correlation, we propose a novel matrix factorization method, so-called collaborative matrix factorization with soft regularization (SRCMF). SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix factorization. In contrast to general collaborative matrix factorization, the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate, not identical. Specifically, SRCMF obtains low-rank feature representations of drug similarity and target similarity, and then uses a soft regularization term to constrain the approximation between drug (target) similarity features and drug (target) potential features of DTI. To comprehensively evaluate the prediction performance of SRCMF, we conduct cross-validation experiments under three different settings. In terms of the area under the precision-recall curve (AUPR), SRCMF achieves better prediction results than six state-of-the-art methods. Besides, under different noise levels of similarity data, the prediction performance of SRCMF is much better than that of collaborative matrix factorization. In conclusion, SRCMF is robust leading to performance improvement in drug-target interaction prediction.

Keywords

drug-target interactioncollaborative matrix factorizationsoft regularizationnoisy data

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Journal of Computer Science and Technology
Volume 36 Issue 2,
March 2021
Pages 310-322
DOI: 10.1007/s11390-021-0844-8
Cite this article:
Gao L-G, Yang M-Y, Wang J-X. Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction. Journal of Computer Science and Technology, 2021, 36(2): 310-322. https://doi.org/10.1007/s11390-021-0844-8

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Received: 29 July 2020
Accepted: 09 March 2021
Published: 05 March 2021
©Institute of Computing Technology, Chinese Academy of Sciences 2021
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