DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Author(s) -
Rawan S. Olayan,
Haitham Ashoor,
Vladimir B. Bajić
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty417
Subject(s) - computer science , machine learning , graph , artificial intelligence , drug discovery , drug target , data mining , theoretical computer science , bioinformatics , chemistry , biology , biochemistry
In our study (Olayan et al., 2018), we performed 96 computational experiments, including six that are related to the COSINE method (Lim et al., 2016). Re-evaluating all numerical results we reported, we found that out of the six tests (5 cross-validation tests and 1 hold-out test) we performed for the COSINE method, the performance of COSINE in two of these tests, both related to the DrugBank_FDA dataset, should be corrected. This implied a few corrections in the article. The repeated analysis confirms that the original qualitative conclusions regarding the newly introduced DDR method stands unaltered. All necessary changes are implemented in the article and associated Supplementary material. The article has also now been updated online. Acknowledgment
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