A multiple kernel learning algorithm for drug-target interaction prediction

Nascimento, André; Prudêncio, Ricardo B. C.; Costa, Ivan G.

doi:10.1186/s12859-016-0890-3

Cited by 170 publications

(116 citation statements)

References 56 publications

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“…With an appropriate kernel function, the original training data are mapped into a higher dimension to solve non‐linear problems. Here, in this work, we chose the pairwise kernel as our kernel function, which has been successfully applied in the predictions of protein‐protein interactions and drug‐target interactions …”

Section: Methodsmentioning

confidence: 99%

“…Here, in this work, we chose the pairwise kernel as our kernel function, which has been successfully applied in the predictions of protein-protein interactions 24 and drug-target interactions. 25 In addition, an error penalty parameter denoted as C, where C > 0, is called the soft-margin constant. To achieve satisfying classification result, the parameters in SVM should be chosen appropriately.…”

Section: Support Vector Machinementioning

confidence: 99%

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Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

et al. 2018

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Summary What is known and objective Drug‐drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity‐based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity‐based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures. Method In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature‐reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well‐known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM. Result The 10‐fold cross‐validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity‐based SVM prediction is highly useful for identifying DDI. Conclusion in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.

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Section: Methodsmentioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

et al. 2018

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“…In Table 2, objects are new indicates that no corresponding DTIs in the training data, and known vice versa. In order to facilitate the comparison with other methods, we followed previous studies [32,[56][57][58] as the benchmark and conducted the 10-fold cross-validations (CVs) test for each experimental setting of each data set, and the above process was repeated 5 times using different random seeds.…”

Section: Experimental Settings and Cross-validationmentioning

confidence: 99%

“…(ii) based on association rules, drugs with similar chemical structures tend to bind similar proteins. It is based on heterogeneous networks of DTIs, using single or fusion similarity measures as features [23,31,32,58].…”

Section: Predictive Ability Of Different Types Of Featuresmentioning

confidence: 99%

DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features

Chu

Zhang

Wang

et al. 2019

Preprint

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Drug-target interactions play a crucial role in target-based drug discovery and exploitation. Computational prediction of DTIs has become a popular alternative strategy to the experimental methods for identification of DTIs of which are both time and resource consuming. However, the performances of the current DTIs prediction approaches suffer from a problem of low precision and high false positive rate. In this study, we aimed to develop a novel DTIs prediction method, named DTI-CDF, for improving the prediction precision based on a cascade deep forest model which integrates hybrid features, including multiple similarity-based features extracted from the heterogeneous graph, fingerprints of drugs, and evolution information of target protein sequences.In the experiments, we built five replicates of 10 fold cross-validations under three different experimental settings of data sets, namely, corresponding DTIs values of certain drugs ( ), targets ( ), or drug-target pairs ( ) in the training set are missed, but existed in the test set. The experimental results show that our proposed approach DTI-CDF achieved significantly higher performance than the state-of-the-art methods.

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“…These similarities play a key role and can be derived from various types of data. Our survey shows that the target similarities are mostly obtained on the basis of genomic sequence, e.g., sequence similarity by structural and physicochemical features or by sequence alignment score, though a few other target features were also used (42), including biological function (43), domain annotation (44), and proximity in the protein-protein interaction network (45). In comparison, data from the drug perspective is much more diverse as shown below.…”

Section: Approachesmentioning

confidence: 99%

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

Cheng

Hao

Takeda

et al. 2017

AAPS J

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Abstract.The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and druginduced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.

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A multiple kernel learning algorithm for drug-target interaction prediction

Cited by 170 publications

References 56 publications

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies

DTI-CDF: a CDF model towards the prediction of DTIs based on hybrid features

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

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