scholarly journals MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation

2018 ◽  
Vol 23 (2) ◽  
pp. 1427-1438 ◽  
Author(s):  
Sheng-Peng Yu ◽  
Cheng Liang ◽  
Qiu Xiao ◽  
Guang-Hui Li ◽  
Ping-Jian Ding ◽  
...  
Keyword(s):  
Matrix Completion ◽  
Disease Association ◽  
Label Propagation ◽  
Novel Method

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

Biomolecules ◽  
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.

scholarly journals A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

2019 ◽  
Vol 16 (2) ◽  
pp. 688-693 ◽  
Author(s):  
Pengyao Ping ◽  
Lei Wang ◽  
Linai Kuang ◽  
Songtao Ye ◽  
Muhammad Faisal Buland Iqbal ◽  
...  
Keyword(s):  
Disease Association ◽  
Association Network ◽  
Novel Method

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

2020 ◽  
Author(s):  
Aanchal Mongia ◽  
Emilie Chouzenoux ◽  
Angshul Majumdar
Keyword(s):  
Binary Data ◽  
Matrix Completion ◽  
Computational Prediction ◽  
Disease Association ◽  
New Drugs ◽  
Low Rank ◽  
Standard Drug ◽  
Neighborhood Graph ◽  
Completion Problem ◽  
Low Rank Matrix Completion

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]

scholarly journals A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association

2019 ◽  
Vol 20 (7) ◽  
pp. 1549 ◽  
Author(s):  
Yang Liu ◽  
Xiang Feng ◽  
Haochen Zhao ◽  
Zhanwei Xuan ◽  
Lei Wang
Keyword(s):  
Computational Models ◽  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Weighted Matrix ◽  
Disease Associations ◽  
Resource Allocation Strategy ◽  
Simulation Results ◽  
Leave One Out ◽  
Treatment Prognosis

Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well.

scholarly journals ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-Tian Wang ◽  
Lei Li ◽  
Cun-Mei Ji ◽  
Chun-Hou Zheng ◽  
Jian-Cheng Ni
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Biological Information ◽  
Disease Similarity ◽  
Kernel Fusion ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Similarity Networks ◽  
Association Data

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA–disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA–disease associations.

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

2020 ◽  
Vol 11 ◽  
Author(s):  
Lin Wang ◽  
Yaguang Chen ◽  
Naiqian Zhang ◽  
Wei Chen ◽  
Yusen Zhang ◽  
...  
Keyword(s):  
Matrix Completion ◽  
Disease Association

Predicting human microbe–disease associations via graph attention networks with inductive matrix completion

2020 ◽  
Author(s):  
Yahui Long ◽  
Jiawei Luo ◽  
Yu Zhang ◽  
Yan Xia
Keyword(s):  
Linear Models ◽  
Matrix Completion ◽  
Critical Role ◽  
Label Propagation ◽  
Human Diseases ◽  
Biomedical Data ◽  
Data Set ◽  
Attention Networks ◽  
Proposed Model ◽  
Disease Associations

Abstract Motivation human microbes play a critical role in an extensive range of complex human diseases and become a new target in precision medicine. In silico methods of identifying microbe–disease associations not only can provide a deep insight into understanding the pathogenic mechanism of complex human diseases but also assist pharmacologists to screen candidate targets for drug development. However, the majority of existing approaches are based on linear models or label propagation, which suffers from limitations in capturing nonlinear associations between microbes and diseases. Besides, it is still a great challenge for most previous methods to make predictions for new diseases (or new microbes) with few or without any observed associations. Results in this work, we construct features for microbes and diseases by fully exploiting multiply sources of biomedical data, and then propose a novel deep learning framework of graph attention networks with inductive matrix completion for human microbe-disease association prediction, named GATMDA. To our knowledge, this is the first attempt to leverage graph attention networks for this important task. In particular, we develop an optimized graph attention network with talking-heads to learn representations for nodes (i.e. microbes and diseases). To focus on more important neighbours and filter out noises, we further design a bi-interaction aggregator to enforce representation aggregation of similar neighbours. In addition, we combine inductive matrix completion to reconstruct microbe-disease associations to capture the complicated associations between diseases and microbes. Comprehensive experiments on two data sets (i.e. HMDAD and Disbiome) demonstrated that our proposed model consistently outperformed baseline methods. Case studies on two diseases, i.e. asthma and inflammatory bowel disease, further confirmed the effectiveness of our proposed model of GATMDA. Availability python codes and data set are available at: https://github.com/yahuilong/GATMDA. Contact [email protected].

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