2018
DOI: 10.2174/1389203718666161114111656
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RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information

Abstract: Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

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Cited by 100 publications
(45 citation statements)
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“…Predictions from the drug and target sides are then averaged to get the final results. [22], SRP [45] Neighborhood methods use relatively simple similarity functions to perform predictions BLMs Bleakley et al [46], LapRLS [47], RLS-avg and RLS-kron [48], BLM-NII [49] BLMs perform two sets of predictions, one from the drug side and one from the target side, and then aggregates these predictions to give the final prediction scores Network diffusion NBI [50], Wang et al [51], NRWRH [52], PSL [53], DASPfind [54] Network diffusion methods investigate graph-based techniques to predict new interactions Matrix factorization KBMF2K [55], PMF [56], CMF [57], WGRMF [58], NRLMF [59], DNILMF [60] Matrix factorization finds two latent feature matrices that, when multiplied together, reconstruct the interaction matrix Feature-based classification He et al [61], Yu et al [62], Fuzzy KNN [63], Ezzat et al [64], EnsemDT [65], SITAR [66], RFDT [78], PDTPS [81], ER-Tree [83], SCCA [84], MH-L1SVM [86] Feature-based classification methods are those that need the drug-target pairs to be explicitly represented as fixed-length feature vectors Specifically, assuming a bipartite DTI network, the algorithm tries to predict whether the edge e ij exists between drug d i and target t j . The following steps are performed:…”
Section: Svm-based Blmsmentioning
confidence: 99%
See 1 more Smart Citation
“…Predictions from the drug and target sides are then averaged to get the final results. [22], SRP [45] Neighborhood methods use relatively simple similarity functions to perform predictions BLMs Bleakley et al [46], LapRLS [47], RLS-avg and RLS-kron [48], BLM-NII [49] BLMs perform two sets of predictions, one from the drug side and one from the target side, and then aggregates these predictions to give the final prediction scores Network diffusion NBI [50], Wang et al [51], NRWRH [52], PSL [53], DASPfind [54] Network diffusion methods investigate graph-based techniques to predict new interactions Matrix factorization KBMF2K [55], PMF [56], CMF [57], WGRMF [58], NRLMF [59], DNILMF [60] Matrix factorization finds two latent feature matrices that, when multiplied together, reconstruct the interaction matrix Feature-based classification He et al [61], Yu et al [62], Fuzzy KNN [63], Ezzat et al [64], EnsemDT [65], SITAR [66], RFDT [78], PDTPS [81], ER-Tree [83], SCCA [84], MH-L1SVM [86] Feature-based classification methods are those that need the drug-target pairs to be explicitly represented as fixed-length feature vectors Specifically, assuming a bipartite DTI network, the algorithm tries to predict whether the edge e ij exists between drug d i and target t j . The following steps are performed:…”
Section: Svm-based Blmsmentioning
confidence: 99%
“…Rotation Forest-based Predictor of Drug-Target Interactions (RFDT) [78] uses yet another ensemble learning technique to predict DTIs. In particular, a variant based on Rotation Forest [79] was used.…”
Section: Rotation Forest-based Predictor Of Drug-target Interactionsmentioning
confidence: 99%
“…The number of drugs was 445, 210, 233 and 54, and the number of target proteins was 664, 204, 95 and 26 in these benchmark data sets, respectively. Among these data, 5127 pairs of drug-target were confirmed to interact with each other, corresponding to 2926, 1476, 635 and 90 pairs in four data sets, respectively [25].…”
Section: Case Studymentioning
confidence: 96%
“…DTI prediction in silicon is one of the effective methods (Liu et al, 2012), and machine learning is a prevalent way (Yan et al, 2019). Support vector machine (SVM) (Keum and Nam, 2017) and random forest (RF) (Wang et al, 2018;Strobl et al, 2019) are often used as predictors in existing research (Olayan et al, 2018). Although these methods are effective, shallow learning models may simplify the relationship between drugs and targeted proteins (Nanni et al, 2020), which are limited by the size of the dataset (Keogh and Mueen, 2009).…”
Section: Introductionmentioning
confidence: 99%