Optimizing drug–target interaction prediction based on random walk on heterogeneous networks

Seal, Abhik; Ahn, Yong‐Yeol; Wild, David

doi:10.1186/s13321-015-0089-z

Cited by 57 publications

(43 citation statements)

References 16 publications

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“…There are a great number of successful applications in the network analysis [35–38]. In random walk, a random walker starts from an initial node, and moves to neighbors with the probability μ and moves back to the initial node with the probability 1 − μ .…”

Section: Methodsmentioning

confidence: 99%

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

et al. 2017

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BackgroundDrug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance.ResultsSince many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances.ConclusionsThe experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1415-9) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

et al. 2017

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“…In each test of ten, a subset was used as a gold standard for testing while the nine remaining subsets of as well as were used as the training set. We used Area Under the Receiver Operating Characteristic Curve (AUC ROC) (Cheng, et al, 2012;Seal, et al, 2015), to assess the quality of the predictions. In practice, AUC ROC scores are calculated by the ROC JAVA library (https://github.com/kboyd/Roc) and Weka evaluation package (Holmes, et al, 1994).…”

Section: Validation and Evaluation Metricsmentioning

confidence: 99%

“…Different from the binary judgement made based on the classification models, inference-based models often utilize interactions, similarities and correlations between drugs and targets to predict a confidence score for a potential drug-target association. In general, the methods can be categorized to: 1) "guilt-by-association"based (Alaimo, et al, 2013;Cheng, et al, 2012;Wang, et al, 2013;Zong, et al, 2017), 2) random walk-based (Cheng, et al, 2012;Seal, et al, 2015), 3) similarity-based (Cheng, et al, 2012), and 4) statistical analysis-based (Cheng, et al, 2016). Since the information used in the inference-based methods can be easily pulled from networks, heterogeneous networks often serve as the input (Alaimo, et al, 2013;Cheng, et al, 2012;Cheng, et al, 2016;Luo, et al, 2017;Seal, et al, 2015;Wang, et al, 2013;Zong, et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…For classification methods, the unknown drug-target pairs are considered as negative samples and the random selection will inevitably include false negative samples for training, which will bias the prediction results (Chen, et al, 2015;Ding, et al, 2013;Peng, et al, 2017). For the inference-based methods, new drugs/targets that previously do not interact with targets/drugs (a.k.a., isolated nodes) cannot be predicted (Chen, et al, 2015;Cheng, et al, 2012;Seal, et al, 2015). The booming of big data also brings opportunities as well as challenges in drug-target prediction.…”

Section: Introductionmentioning

confidence: 99%

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Scalable and Accurate Drug–target Prediction Based on Heterogeneous Bio-linked Network Mining

Zong¹,

Wong²,

Ngo³

et al. 2019

Preprint

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Motivation:Despite the existing classification-and inference-based machine learning methods that show promising results in drug-target prediction, these methods possess inevitable limitations, where: 1) results are often biased as it lacks negative samples in the classification-based methods, and 2) novel drug-target associations with new (or isolated) drugs/targets cannot be explored by inference-based methods. As big data continues to boom, there is a need to study a scalable, robust, and accurate solution that can process large heterogeneous datasets and yield valuable predictions. Results: We introduce a drug-target prediction method that improved our previously proposed method from the three aspects: 1) we constructed a heterogeneous network which incorporates 12 repositories and includes 7 types of biomedical entities (#20,119 entities , # 194,296 associations), 2) we enhanced the feature learning method with Node2Vec, a scalable state-of-art feature learning method, 3) we integrate the originally proposed inference-based model with a classification model, which is further fine-tuned by a negative sample selection algorithm. The proposed method shows a better result for drug-target association prediction: 95.3% AUC ROC score compared to the existing methods in the 10-fold cross-validation tests. We studied the biased learning/testing in the network-based pairwise prediction, and conclude a best training strategy. Finally, we conducted a disease specific prediction task based on 20 diseases. New drug-target associations were successfully predicted with AUC ROC in average, 97.2% (validated based on the DrugBank 5.1.0). The experiments showed the reliability of the proposed method in predicting novel drug-target associations for the disease treatment.

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“…Alaimo [19] extended Cheng's method to integrate chemical and target similarity and showed that that the performance of the method is superior to Cheng's model. Chen [20] and Seal [21] have used random walk with restart (RWR) based method to predict drug target interactions on a heterogeneous network made up of drug-drug similarity, protein-protein similarity and bipartite graph between drugs and targets. Seal et al have extended the method by optimizing a parameter η which showed that the performance of RWR is independent of the choice of using Chemical fingerprint features.…”

Section: Introductionmentioning

confidence: 99%

Netpredictor: R and Shiny package to perform Drug-Target Bipartite network analysis and prediction of missing links

Seal

Wild

2016

Preprint

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Netpredictor is an R package for prediction of missing links in any given bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download from github Netpredictor repository and Shiny Netpredictor repository respectively.

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Optimizing drug–target interaction prediction based on random walk on heterogeneous networks

Cited by 57 publications

References 16 publications

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Scalable and Accurate Drug–target Prediction Based on Heterogeneous Bio-linked Network Mining

Netpredictor: R and Shiny package to perform Drug-Target Bipartite network analysis and prediction of missing links

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