Drug repositioning based on bounded nuclear norm regularization

Yang, Mingzhu; Luo, Huimin; Li, Yaohang; Wang, Jianxin

doi:10.1093/bioinformatics/btz331

Cited by 140 publications

(87 citation statements)

References 28 publications

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“…In addition, other integration methods of receptor similarity also should be considered in the future. Finally, other latest matrix factorization methods also should be considered, such as DNRLMF-MDA [37], DRRS [38], SIMCLDA [39] and BNNR [40]. Therefore, we would like to develop a more effective method for predicting virus-receptor interactions by addressing the above limitations in the future.…”

Section: Resultsmentioning

confidence: 99%

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

Cheng

Duan

et al. 2019

BMC Bioinformatics

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BackgroundViral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited.ResultIn this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF).ConlusionThe experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

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Section: Resultsmentioning

confidence: 99%

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

Cheng

Duan

et al. 2019

BMC Bioinformatics

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“…To evaluate the performance of GR1BMC, we compare the results of cross-validation experiments with those of the latest methods proposed for drug-disease association prediction: Bounded nuclear norm regularization (BNNR) Yang et al (2019b), Heterogeneous Network for drug-Disease association prediction (HNRD) Wang et al (2019) and drug repositioning recommendation system (DRRS) Luo et al (2018). BNNR and DRRS are the closest in terms of formulation used to model the problem.…”

Section: Comparison With Benchmark Techniquesmentioning

confidence: 99%

“…There are relatively few works modelling the prediction task using nuclear norm minimization. Luo et al (2018) and Yang et al (2019b) deploy nuclear norm minimization on a heterogeneous network matrix obtained by integrating drug similarity, disease similarity, association matrix and its transpose; the latter work additionally handles the noise originating from similarities which violate the low-rankness and restrict the predicted values to be in range [0,1]. But, the low-rank property of the heterogeneous matrix is unexplained in both the works, although it is a crucial assumption behind nuclear norm minimization.…”

Section: Introductionmentioning

confidence: 99%

Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion

Mongia

Chouzenoux

Majumdar

2020

Preprint

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MotivationInvestigation of existing drugs is an effective alternative to discovery of new drugs for treating diseases. This task of drug re-positioning can be assisted by various kinds of computational methods to predict the best indication for a drug given the open-source biological datasets. Owing to the fact that similar drugs tend to have common pathways and disease indications, the association matrix is assumed to be of low-rank structure. Hence, the problem of drug-disease association prediction can been modelled as a low-rank matrix-completion problem.ResultsIn this work, we propose a novel matrix completion framework which makes use of the sideinformation associated with drugs/diseases for the prediction of drug-disease indications modelled as neighborhood graph: Graph regularized 1-bit matrix compeltion (GR1BMC). The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into account all the constraints including the neighborhood graph incorporation and restricting predicted scores within the specified range. The results of the proposed algorithm have been validated on two standard drug-disease association databases (Fdataset and Cdataset) by evaluating the AUC across the 10-fold cross validation splits. The usage of the method is also evaluated through a case study where top 5 indications are predicted for novel drugs and diseases, which then are verified with the CTD database. The results of these experiments demonstrate the practical usage and superiority of the proposed approach over the benchmark methods.Contactaanchalm@iiitd.ac.in

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“…The singular value thresholding algorithm (SVT) [13] was implemented to complete the missing entries of a drug-disease association matrix. Yang et al further proposed a bounded nuclear norm regularization (BNNR) model [14], not only tolerating the noisy similarities of drugs and diseases by employing regularization, but also ensuring that all predicted values are within the interval of [0, 1]. However, the computational cost of both DRRS and BNNR increases sharply when target (protein/gene) information is incorporated into the heterogeneous drug-disease network.…”

Section: Introductionmentioning

confidence: 99%

“…This can significantly reduce the computational complexity for matrix completion. Meanwhile, a BNNR model [14] developed in our previous work is implemented to fill out the missing entries in the block adjacency matrix of these networks. We evaluate the performance of OMC2 and OMC3 in three different datasets and compare them with five latest approaches for drug repositioning.…”

Section: Introductionmentioning

confidence: 99%

Overlap matrix completion for predicting drug-associated indications

Yang

Luo

et al. 2019

PLoS Comput Biol

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Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug-or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications. Author summaryThis work introduces a computational approach, namely overlap matrix completion (OMC), to predict potential associations between drugs and diseases. The novelty of OMC lies in constructing an efficient framework of incorporating multiple types of prior information in bilayer and tri-layer networks. OMC for bilayer networks (OMC2) can approximate the low-rank structures of the drug-disease association matrices from both PLOS Computational Biology | https://doi.China drug-side and disease-side. In addition, we further improve the prediction accuracy by extending OMC to handle tri-layer networks and develop its corresponding algorithm (OMC3). To evaluate the performance of OMC2 and OMC3, we conduct 10-fold crossvalidation and de novo experiments on three datasets. Our computational results demonstrate that both OMC2 and OMC3 generally outperform five state-of-the-art methods in terms of ROC curve, PR curve, and top-ranked predictions.OMC for predicting drug-associated indications PLOS Computational Biology | https://doi.

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Drug repositioning based on bounded nuclear norm regularization

Cited by 140 publications

References 28 publications

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning

Computational prediction of Drug-Disease association based on Graph-regularized one bit Matrix completion

Overlap matrix completion for predicting drug-associated indications

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