Multi-algorithm and multi-model based drug target prediction and web server

Liu, Ying-tao; Li, Yang; Huang, Zifu; Xu, Zhijian; Yang, Zhuo; Chen, Zhu-xi; Chen, Kaixian; Shi, Jiye; Zhu, Weiliang

doi:10.1038/aps.2013.153

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…Indeed, many Web servers have been developed to facilitate this task for practitioners who wish to perform it on a global scale [26]. Examples of such Web servers include DINIES [144], BalestraWeb [145] and SuperPred [146] among several others [54,[147][148][149][150].…”

Section: Discussionmentioning

confidence: 99%

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

Ezzat¹,

Wu²,

Li³

et al. 2018

Briefings in Bioinformatics

232

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: Discussionmentioning

confidence: 99%

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

Ezzat¹,

Wu²,

Li³

et al. 2018

Briefings in Bioinformatics

232

184

View full text Add to dashboard Cite

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“…In recent years, various computational strategies for predicting potential druggable proteins have emerged, which commonly use the sequence, structural, and functional features of proteins as input [5] , [16] , [17] , [18] , [19] but also system-level properties such as network topological features [20] , [21] , [22] , [23] . Various machine learning (ML) algorithms have been employed to develop in silico models, including support vector machine (SVM) [24] , [25] , [26] , [27] , neural network (NN) [28] , [29] , naive Bayes (NB) [30] , [31] , logistic regression (LR) [32] , hidden Markov model (HMM) [33] , random forest (RF) [34] , and ensemble methods [35] , [36] , [37] .…”

Section: Introductionmentioning

confidence: 99%

The applications of deep learning algorithms on in silico druggable proteins identification

Liu

et al. 2022

Journal of Advanced Research

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“…Thus, the DTIs prediction problem can be transformed into a binary classification task. Some typical classifiers have been used for DTIs identification, including the support vector machines [9], the decision tree [10], [11], the random forest [12], [13] and the logistic regression [14], [15]. In addition, as one major machine learning approach, chemogenomic approaches have been used for DTIs prediction by combining chemical structure of drugs and genomic sequence of targets [16].…”

Section: Introductionmentioning

confidence: 99%

Drug-Target Interaction Prediction Based on Multi-Similarity Fusion and Sparse Dual-Graph Regularized Matrix Factorization

Lian

Wang

et al. 2021

IEEE Access

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Drug-target interactions (DTIs) prediction plays a vital role in drug discovery and design. Current studies typically use only standard drug similarity and target similarity, but the influence of known interactions has not been taken into account. In this paper, we propose an ensembled computational approach called multi-similarity fusion and sparse dual-graph regularized matrix factorization (MSDGRMF) for DTIs prediction. Specifically, different similarities are integrated to mine more useful information from the known interactions. The dual-graph regularized matrix factorization is used to predict the DTIs, in which the manifold learning is used for the low-dimensional representation of the drugs and targets data. In addition, not all the information of drug pairs and target pairs is useful. Thus, the useless information is discarded by sparse process. The proposed MSDGRMF is evaluated and compared on some benchmark datasets. Comparison results show that the MSDGRMF is better than some state-of-the-art approaches. More importantly, the proposed method can contribute to predicting potential DTIs.INDEX TERMS Drug-target interactions prediction, multi-similarity fusion, dual-graph regularized matrix factorization, manifold learning.

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Multi-algorithm and multi-model based drug target prediction and web server

Cited by 7 publications

References 47 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

The applications of deep learning algorithms on in silico druggable proteins identification

Drug-Target Interaction Prediction Based on Multi-Similarity Fusion and Sparse Dual-Graph Regularized Matrix Factorization

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