Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm

Luo, Huimin; Wang, Jianxin; Li, Min; Liu, Junwei; Peng, Xiaoqing; Wu, Fang‐Xiang; Pan, Yi

doi:10.1093/bioinformatics/btw228

Cited by 354 publications

(289 citation statements)

References 24 publications

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“…In order to control or prevent the diseases, people try to use disease similarity and drug repositioning to gain deeper insights into pathogenic mechanisms of complex diseases [1–3]. However, in clinical practice, there are particularity and complexity between similar diseases, and there are limitations in drug repositioning.…”

Section: Introductionmentioning

confidence: 99%

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu

Fei

et al. 2018

BMC Syst Biol

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BackgroundOsteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.ResultsIn this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.ConclusionCompared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

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Section: Introductionmentioning

confidence: 99%

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Liu

Fei

et al. 2018

BMC Syst Biol

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“…Nonetheless, they usually do not guarantee that the trained models are unbiased with respect to unseen data. For instance, Luo et al 11 use an independent set of drug-disease associations, yet, 95% of the drugs in the independent set are also in the original data set (109 out of 115). On the other hand, Gottlieb et al 4 create the folds such that 10% of the drugs are hidden instead of 10% of the drug-disease pairs, but they do not ensure that the drugs used to train the model are disjoint from the drugs in the test set.…”

Section: Revisiting Cross Validation Using Disjoint Foldsmentioning

confidence: 99%

“…These studies often combine various features including but not limited to chemical 2D fingerprint similarity, overlap or interaction network closeness of drug targets and correlation between drug side effects and build a machine learning model based on different algorithms, such as support vector machines, random forests and logistic regression classifiers. [4][5][6][7][8][9][10][11] The proposed models are then compared in a cross validation setting, in which a portion of the known drug-disease associations are hidden during training and used for the validation afterwards. The areas under reciver operating characteristic (ROC) curves in the cross validation analysis reported for these models range between 75-95%, suggesting that some of these models can accurately identify novel drugdisease associations.…”

Section: Introductionmentioning

confidence: 99%

Reproducible Drug Repurposing: When Similarity Does Not Suffice

Güney

2016

Biocomputing 2017

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Repurposing existing drugs for new uses has attracted considerable attention over the past years. To identify potential candidates that could be repositioned for a new indication, many studies make use of chemical, target, and side effect similarity between drugs to train classifiers. Despite promising prediction accuracies of these supervised computational models, their use in practice, such as for rare diseases, is hindered by the assumption that there are already known and similar drugs for a given condition of interest. In this study, using publicly available data sets, we question the prediction accuracies of supervised approaches based on drug similarity when the drugs in the training and the test set are completely disjoint. We first build a Python platform to generate reproducible similaritybased drug repurposing models. Next, we show that, while a simple chemical, target, and side effect similarity based machine learning method can achieve good performance on the benchmark data set, the prediction performance drops sharply when the drugs in the folds of the cross validation are not overlapping and the similarity information within the training and test sets are used independently. These intriguing results suggest revisiting the assumptions underlying the validation scenarios of similarity-based methods and underline the need for unsupervised approaches to identify novel drug uses inside the unexplored pharmacological space. We make the digital notebook containing the Python code to replicate our analysis that involves the drug repurposing platform based on machine learning models and the proposed disjoint cross fold generation method freely available at github. com/emreg00/repurpose.

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“…Therefore, how to deal with this problem becomes an emerging issue. Over decades, different computational methods and tools [5][6][7][8][9][10][11][12][13] have been developed to predict large-scale potential DTIs and drug repositing through the unremitting efforts of a large number of researchers and organizations under the development of computing technology.…”

Section: Introductionmentioning

confidence: 99%

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng

Lan

et al. 2017

Complexity

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It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 354 publications

References 24 publications

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Reproducible Drug Repurposing: When Similarity Does Not Suffice

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

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