Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network

Zhang, Wen; Yue, Xiang; Huang, Feng; Liu, Ruoqi; Chen, Yanlin; Ruan, Chunyang

doi:10.1016/j.ymeth.2018.06.001

Cited by 77 publications

(32 citation statements)

References 51 publications

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“…Liang et al integrated drug chemical information, target domain information and gene ontology annotation information, and proposed a Laplacian regularized sparse subspace learning method (LRSSL) to predict drug-disease associations [ 17 ]. Zhang et al introduced a linear neighborhood similarity [ 18 ] and a network topological similarity [ 19 ], then proposed a similarity constrained matrix factorization method (SCMFDD) to predict drug-disease associations by making use of known drug-disease associations, drug features and disease semantic information [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition

Liu

Yue

2019

BMC Bioinformatics

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Background In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. Results In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. Conclusions The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition

Liu

Yue

2019

BMC Bioinformatics

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“…It is important to evaluate the results of a new model, and several evaluation metrics are available. Sensitivity (Sn), specificity (Sp), accuracy (Acc), and Mathew's correlation coefficient (MCC) are often used to evaluate the quality of a model in machine learning (Liu B. et al, 2019;Cheng et al, 2012;Cheng et al, 2016;Ding et al, 2016b;Mariani et al, 2017;Ding et al, 2017;Wei et al, 2017a;Wei et al, 2017b;Hu et al, 2018;Zhang et al, 2018c;Ding et al, 2019;Shan et al, 2019;Tan et al, 2019b;Cheng et al, 2019b). These metrics are formulated as follows: These metrics are commonly used in machine learning.…”

Section: Performance Evaluationmentioning

confidence: 99%

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

et al. 2020

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Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.

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“…In another case, Jahchan et al (2013) applied a drug repurposing bioinformatics method to identifying antidepressant drugs for the treatment of small cell lung cancer through querying a large compendium of gene expression profiles. Although many machine learning-based methods have been developed by using features (Zhang et al, 2017(Zhang et al, , 2018a(Zhang et al, ,b, 2019, more and more literature supports the usage of CMap for drug repositioning; despite this, there are still problems. A candidate can often be strengthened using independent disease signatures.…”

Section: Introductionmentioning

confidence: 99%

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Gao

2020

Front. Bioeng. Biotechnol.

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Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.

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Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network

Cited by 77 publications

References 51 publications

Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition

Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

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