PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach

AbstractMotivationInvestigation 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 [email protected]

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MiRNA-Disease Association Prediction via Non-negative Matrix Factorization based Matrix Completion

Signal Processing ◽

10.1016/j.sigpro.2021.108312 ◽

2021 ◽

pp. 108312

Author(s):

Xiao Zheng ◽

Chujie Zhang ◽

Cheng Wan

Keyword(s):

Matrix Factorization ◽

Matrix Completion ◽

Disease Association ◽

Non Negative Matrix Factorization

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QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion

Frontiers in Genetics ◽

10.3389/fgene.2020.594796 ◽

2020 ◽

Vol 11 ◽

Author(s):

Lin Wang ◽

Yaguang Chen ◽

Naiqian Zhang ◽

Wei Chen ◽

Yusen Zhang ◽

...

Keyword(s):

Matrix Completion ◽

Disease Association

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HOPMCLDA: predicting lncRNA-disease association through High-Order Proximity and Matrix Completion

Molecular Omics ◽

10.1039/d1mo00138h ◽

2021 ◽

Author(s):

Guobo Xie ◽

Yinting Zhu ◽

Zhiyi Lin ◽

Yuping Sun ◽

Guosheng Gu ◽

...

Keyword(s):

Matrix Completion ◽

Noncoding Rnas ◽

Complex Diseases ◽

Disease Association ◽

High Order ◽

Long Noncoding Rnas ◽

Biological Processes

In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) is essential to biological processes, diagnosis, and treatments associated with complex diseases. However, infering the associations between diseases...

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Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

Biomolecules ◽

10.3390/biom12010064 ◽

2022 ◽

Vol 12 (1) ◽

pp. 64

Author(s):

Chen Jin ◽

Zhuangwei Shi ◽

Ken Lin ◽

Han Zhang

Keyword(s):

Network Architecture ◽

Matrix Completion ◽

Disease Association ◽

Attention Mechanism ◽

Label Propagation ◽

Disease Prediction ◽

Learning Approaches ◽

Neural Network Architecture ◽

Disease Associations ◽

Collaborative Training

Many studies have clarified that microRNAs (miRNAs) are associated with many human diseases. Therefore, it is essential to predict potential miRNA-disease associations for disease pathogenesis and treatment. Numerous machine learning and deep learning approaches have been adopted to this problem. In this paper, we propose a Neural Inductive Matrix completion-based method with Graph Autoencoders (GAE) and Self-Attention mechanism for miRNA-disease associations prediction (NIMGSA). Some of the previous works based on matrix completion ignore the importance of label propagation procedure for inferring miRNA-disease associations, while others cannot integrate matrix completion and label propagation effectively. Varying from previous studies, NIMGSA unifies inductive matrix completion and label propagation via neural network architecture, through the collaborative training of two graph autoencoders. This neural inductive matrix completion-based method is also an implementation of self-attention mechanism for miRNA-disease associations prediction. This end-to-end framework can strengthen the robustness and preciseness of both matrix completion and label propagation. Cross validations indicate that NIMGSA outperforms current miRNA-disease prediction methods. Case studies demonstrate that NIMGSA is competent in detecting potential miRNA-disease associations.

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SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.617569 ◽

2021 ◽

Vol 9 ◽

Author(s):

Lei Li ◽

Zhen Gao ◽

Chun-Hou Zheng ◽

Yu Wang ◽

Yu-Tian Wang ◽

...

Keyword(s):

Cross Validation ◽

Matrix Completion ◽

Disease Association ◽

Human Diseases ◽

Data Types ◽

Similarity Network ◽

Multiple Data ◽

Disease Similarity ◽

Leave One Out ◽

Novel Model

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA–disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA–disease.

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Predicting miRNA–disease association based on inductive matrix completion

Bioinformatics ◽

10.1093/bioinformatics/bty503 ◽

2018 ◽

Cited By ~ 82

Author(s):

Xing Chen ◽

Lei Wang ◽

Jia Qu ◽

Na-Na Guan ◽

Jian-Qiang Li

Keyword(s):

Matrix Completion ◽

Disease Association

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mHMDA: Human Microbe-Disease Association Prediction by Matrix Completion and Multi-Source Information

IEEE Access ◽

10.1109/access.2019.2930453 ◽

2019 ◽

Vol 7 ◽

pp. 106687-106693

Author(s):

Chuanyan Wu ◽

Rui Gao ◽

Yusen Zhang

Keyword(s):

Matrix Completion ◽

Disease Association ◽

Source Information

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Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

Bioinformatics ◽

10.1093/bioinformatics/btz965 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2538-2546 ◽

Cited By ~ 9

Author(s):

Jin Li ◽

Sai Zhang ◽

Tao Liu ◽

Chenxi Ning ◽

Zhuoxuan Zhang ◽

...

Keyword(s):

Characteristic Curve ◽

Matrix Completion ◽

Disease Association ◽

Supplementary Information ◽

Convolutional Network ◽

Convolutional Networks ◽

Feature Representations ◽

Disease Similarity ◽

Disease Associations ◽

Association Data

Abstract Motivation Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA. Results We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs. Availability and implementation https://github.com/ljatynu/NIMCGCN/ Supplementary information Supplementary data are available at Bioinformatics online.

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