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

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

International Journal of Molecular Sciences ◽

10.3390/ijms20071549 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1549 ◽

Cited By ~ 6

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.

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Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

JMIR Medical Informatics ◽

10.2196/14502 ◽

2019 ◽

Vol 7 (4) ◽

pp. e14502

Author(s):

Po-Ting Lai ◽

Wei-Liang Lu ◽

Ting-Rung Kuo ◽

Chia-Ru Chung ◽

Jen-Chieh Han ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Network Architecture ◽

Disease Association ◽

Support Vector ◽

Context Aware ◽

Neural Network Architecture ◽

Large Margin

Background Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. Objective As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. Methods In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. Results Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. Conclusions To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.743665 ◽

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.

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

Briefings in Bioinformatics ◽

10.1093/bib/bbaa146 ◽

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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Deep Neural Network Architecture Search for Wearable Heart Rate Estimations

Studies in Health Technology and Informatics - Public Health and Informatics ◽

10.3233/shti210366 ◽

2021 ◽

Author(s):

Daniel Ray ◽

Tim Collins ◽

Prasad Ponnapalli

Keyword(s):

Neural Network ◽

Heart Rate ◽

Deep Learning ◽

Network Architecture ◽

Deep Neural Network ◽

State Of The Art ◽

Data Driven ◽

Network Architectures ◽

Learning Approaches ◽

Neural Network Architecture

Extracting accurate heart rate estimations from wrist-worn photoplethysmography (PPG) devices is challenging due to the signal containing artifacts from several sources. Deep Learning approaches have shown very promising results outperforming classical methods with improvements of 21% and 31% on two state-of-the-art datasets. This paper provides an analysis of several data-driven methods for creating deep neural network architectures with hopes of further improvements.

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

Journal of Cellular and Molecular Medicine ◽

10.1111/jcmm.14048 ◽

2018 ◽

Vol 23 (2) ◽

pp. 1427-1438 ◽

Cited By ~ 11

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

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DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion

Bioinformatics ◽

10.1093/bioinformatics/btaa062 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2839-2847 ◽

Cited By ~ 1

Author(s):

Wenjuan Zhang ◽

Hunan Xu ◽

Xiaozhong Li ◽

Qiang Gao ◽

Lin Wang

Keyword(s):

Drug Repositioning ◽

Matrix Completion ◽

Disease Association ◽

Data Sources ◽

Supplementary Information ◽

Similarity Matrix ◽

Latent Factors ◽

Discovery Research ◽

Disease Associations ◽

Novel Drug

Abstract Motivation One of the most important problems in drug discovery research is to precisely predict a new indication for an existing drug, i.e. drug repositioning. Recent recommendation system-based methods have tackled this problem using matrix completion models. The models identify latent factors contributing to known drug-disease associations, and then infer novel drug-disease associations by the correlations between latent factors. However, these models have not fully considered the various drug data sources and the sparsity of the drug-disease association matrix. In addition, using the global structure of the drug-disease association data may introduce noise, and consequently limit the prediction power. Results In this work, we propose a novel drug repositioning approach by using Bayesian inductive matrix completion (DRIMC). First, we embed four drug data sources into a drug similarity matrix and two disease data sources in a disease similarity matrix. Then, for each drug or disease, its feature is described by similarity values between it and its nearest neighbors, and these features for drugs and diseases are mapped onto a shared latent space. We model the association probability for each drug-disease pair by inductive matrix completion, where the properties of drugs and diseases are represented by projections of drugs and diseases, respectively. As the known drug-disease associations have been manually verified, they are more trustworthy and important than the unknown pairs. We assign higher confidence levels to known association pairs compared with unknown pairs. We perform comprehensive experiments on three benchmark datasets, and DRIMC improves prediction accuracy compared with six stat-of-the-art approaches. Availability and implementation Source code and datasets are available at https://github.com/linwang1982/DRIMC. Supplementary information Supplementary data are available at Bioinformatics online.

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NCMCMDA: miRNA–disease association prediction through neighborhood constraint matrix completion

Briefings in Bioinformatics ◽

10.1093/bib/bbz159 ◽

2020 ◽

Cited By ~ 6

Author(s):

Xing Chen ◽

Lian-Gang Sun ◽

Yan Zhao

Keyword(s):

Computational Models ◽

Cross Validation ◽

Matrix Completion ◽

Critical Role ◽

Disease Association ◽

Human Diseases ◽

Superior Performance ◽

Main Task ◽

Constraint Matrix ◽

Disease Associations

Abstract Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse fundamental and important biological processes associated with human diseases. Inferring potential disease related miRNAs and employing them as the biomarkers or drug targets could contribute to the prevention, diagnosis and treatment of complex human diseases. In view of that traditional biological experiments cost much time and resources, computational models would serve as complementary means to uncover potential miRNA–disease associations. In this study, we proposed a new computational model named Neighborhood Constraint Matrix Completion for MiRNA–Disease Association prediction (NCMCMDA) to predict potential miRNA–disease associations. The main task of NCMCMDA was to recover the missing miRNA–disease associations based on the known miRNA–disease associations and integrated disease (miRNA) similarity. In this model, we innovatively integrated neighborhood constraint with matrix completion, which provided a novel idea of utilizing similarity information to assist the prediction. After the recovery task was transformed into an optimization problem, we solved it with a fast iterative shrinkage-thresholding algorithm. As a result, the AUCs of NCMCMDA in global and local leave-one-out cross validation were 0.9086 and 0.8453, respectively. In 5-fold cross validation, NCMCMDA achieved an average AUC of 0.8942 and standard deviation of 0.0015, which demonstrated NCMCMDA’s superior performance than many previous computational methods. Furthermore, NCMCMDA was applied to three different types of case studies to further evaluate its prediction reliability and accuracy. As a result, 84% (colon neoplasms), 98% (esophageal neoplasms) and 98% (breast neoplasms) of the top 50 predicted miRNAs were verified by recent literature.

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Residual Neural Network Model for Detecting Waste Disposing Action in Images

Journal of Electrical Electronics and Informatics ◽

10.24843/jeei.2021.v05.i02.p03 ◽

2021 ◽

Vol 5 (2) ◽

pp. 52

Author(s):

I Made Arsa Suyadnya ◽

Duman Care Khrisne

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Network Architecture ◽

Input Image ◽

Scene Recognition ◽

Learning Approaches ◽

Neural Network Architecture ◽

The World ◽

Disposal Behavior

Waste in general has become a major problem for people around the world. Evidence internationally shows that everyone, or nearly everyone, admits to polluting at some point, with the majority of people littering at least occasionally. This research wants to overcome these problems, by utilizing computer vision and deep learning approaches. This research was conducted to detect the actions carried out by humans in the activities/actions of disposing of waste in an image. This is useful to provide better information for research on better waste disposal behavior than before. We use a Convolutional Neural Network model with a Residual Neural Network architecture to detect the types of activities that objects perform in an image. The result is an artificial neural network model that can label the activities that occur in the input image (scene recognition). This model has been able to carry out the recognition process with an accuracy of 88% with an F1-Score of 0.87.

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