Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa205
Keywords
Drug-target interaction; multi-label learning; label correlation; community detection
Funding
- Ministry of Science and Technology of China [2016YFA0501703]
- National Natural Science Foundation of China [61832019, 61503244]
- Science and Technology Commission of Shanghai Municipality [19430750600]
- Natural Science Foundation of Henan Province [162300410060]
- SJTU JiRLMDS Joint Research Fund
- Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University [YG2017ZD14, ZH2018QNA41, YG2019GD01, YG2019ZDA12]
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This paper introduces a multi-label classification method, DTI-MLCD, for DTI prediction by incorporating community detection methods, and demonstrates its superiority through updated gold standard dataset.
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based models, especially binary classification models, have been developed to predict whether a drug-target pair interacts or not. However, there is still much room for improvement in the performance of current methods. Multi-label learning can overcome some difficulties caused by single-label learning in order to improve the predictive performance. The key challenge faced by multi-label learning is the exponential-sized output space, and considering label correlations can help to overcome this challenge. In this paper, we facilitate multi-label classification by introducing community detection methods for DTI prediction, named DTI-MLCD. Moreover, we updated the gold standard data set by adding 15,000 more positive DTI samples in comparison to the data set, which has widely been used by most of previously published DTI prediction methods since 2008. The proposed DTI-MLCD is applied to both data sets, demonstrating its superiority over other machine learning methods and several existing methods.
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