RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

doi:10.2174/1389203718666161114111656

Cited by 96 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%

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Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

Ezzat¹,

Wu²,

Li³

et al. 2018

Briefings in Bioinformatics

220

184

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Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.

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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%

Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

Ezzat¹,

Wu²,

Li³

et al. 2018

Briefings in Bioinformatics

220

184

View full text Add to dashboard Cite

show abstract

“…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%

DTIRF: Predicting Drug-Target Interactions Based on Improved Rotation Forest from Drug Molecular Structure and Protein Sequence

Wang¹,

Zhang²,

Li³

et al. 2019

Preprint

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Background: The identification and prediction of Drug-Target Interaction (DTI) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. Results: In this article, we design a new in silico approach, named DTIRF, to predict the DTI combine feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features. Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the feature weighted rotation forest is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor data sets. The results of the five-fold cross-validation experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark data sets, respectively. To further validate the performance of the DTIRF, we compare it with other excellent methods and Support Vector Machine (SVM) model. In addition, 7 of the 10 highest predictive scores in predicting novel DTIs were validated by relevant databases. Conclusions: The experimental results of cross-validation indicated that DTIRF is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

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“…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%

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Wang

Yifeng

Lü

et al. 2020

Front. Bioeng. Biotechnol.

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Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins. For each drug molecules, the five different features of drug atoms and the atomic bond relationships are expressed as graphs. The obtained protein features and graph structure are used as the input of convolution neural network and the input of graph convolution neural network, respectively. A prediction model is established to predict the drug affinity by calculating the hidden relationship. In the KIBA data set test experiment, the consistency coefficient of the model is 0.901, which is 0.01 higher than the existing model, and the MSE (mean square error) of the model is 0.126, which is 5% lower than the existing model. In Davis data set test experiment, the consistency coefficient of the model is 0.895, which is 0.006 higher than the existing model, and the MSE of the model is 0.220, which is 4% lower than the existing model. These results show that our proposed method can not only predict the affinity better than those existing models, but also outperform unitary deep learning approaches.

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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.

Cited by 96 publications

References 0 publications

Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

DTIRF: Predicting Drug-Target Interactions Based on Improved Rotation Forest from Drug Molecular Structure and Protein Sequence

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

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