4.7 Article

Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks

Related references

Note: Only part of the references are listed.
Article Biochemical Research Methods

PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features

Cheng Yan et al.

Summary: In this study, a new deep learning method called PDMDA is proposed to accurately predict deep-level miRNA-disease associations. By using graph neural networks (GNNs) and miRNA sequence features, PDMDA can extract valuable information from the feature representations of miRNAs and diseases, leading to efficient predictions.

BIOINFORMATICS (2022)

Article Biochemical Research Methods

Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder

Wei Liu et al.

Summary: In this study, a new computational method called DFELMDA is proposed to predict miRNA-disease associations using deep forest ensemble learning and autoencoder. Results from experiments on the HMDD dataset show that DFELMDA outperforms other methods in terms of performance.

BRIEFINGS IN BIOINFORMATICS (2022)

Article Biochemical Research Methods

Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares

Wengang Wang et al.

Summary: The dysregulation of miRNAs is associated with human complex diseases, and identifying disease-related miRNAs is crucial for disease prevention, diagnosis, and treatment. In this study, a computational framework called MKGAT is proposed to predict miRNA-disease associations using graph attention networks and dual Laplacian regularized least squares. The method achieves high prediction accuracy and has been confirmed by existing databases.

BRIEFINGS IN BIOINFORMATICS (2022)

Article Biochemical Research Methods

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

Xing Chen et al.

Summary: miRNAs play a critical role in human diseases and computational models are effective in predicting potential miRNA-disease associations. The NCMCMDA model showed superior performance in prediction.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemical Research Methods

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

Dayun Liu et al.

Summary: This study developed a computational framework called SMALF to predict unknown miRNA-disease associations by learning latent features of miRNA and disease, achieving better prediction performance compared to other state-of-the-art methods.

BMC BIOINFORMATICS (2021)

Article Biochemical Research Methods

Combined embedding model for MiRNA-disease association prediction

Bailong Liu et al.

Summary: The study confirms the important role of miRNAs in diagnosing and treating diseases. A novel combined embedding model is proposed to predict miRNA-disease associations, showing superior performance compared to other methods.

BMC BIOINFORMATICS (2021)

Article Biochemical Research Methods

Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction

Xinru Tang et al.

Summary: The study developed a model called MMGCN to predict potential miRNA-disease associations, achieving superior performance on two datasets and validating the effectiveness of the multichannel attention mechanism and multisource data in association prediction.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemical Research Methods

A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations

Wen Zhang et al.

Summary: The paper introduces a network link inference method FLNSNLI based on linear neighborhood similarity for predicting miRNA-disease associations, which shows high accuracy and outperforms other methods. It performs well in predicting links for miRNAs or diseases without known associations.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

Article Biochemical Research Methods

Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning

Yu-An Huang et al.

Summary: The study proposes a deep learning model called MVMTMDA to predict MDAs by creating a multiview representation of microRNAs. Experimental results show that the model performs well in predicting MDAs with average area under the ROC curve of 0.8410+/-0.018, 0.8512+/-0.012 and 0.8521+/-0.008 when k is set to 2, 5 and 10, respectively.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemical Research Methods

Ensemble of decision tree reveals potential miRNA-disease associations

Xing Chen et al.

PLOS COMPUTATIONAL BIOLOGY (2019)

Article Medicine, Research & Experimental

Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks

Xiangxiang Zeng et al.

MOLECULAR THERAPY-NUCLEIC ACIDS (2019)

Article Biochemistry & Molecular Biology

HMDD v3.0: a database for experimentally supported human microRNA-disease associations

Zhou Huang et al.

NUCLEIC ACIDS RESEARCH (2019)

Article Biochemistry & Molecular Biology

lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs

Ya-Ru Miao et al.

NUCLEIC ACIDS RESEARCH (2018)

Article Biochemical Research Methods

Prediction of potential disease-associated microRNAs using structural perturbation method

Xiangxiang Zeng et al.

BIOINFORMATICS (2018)

Article Biochemical Research Methods

Predicting miRNA-disease association based on inductive matrix completion

Xing Chen et al.

BIOINFORMATICS (2018)

Article Biochemical Research Methods

MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction

Xing Chen et al.

PLOS COMPUTATIONAL BIOLOGY (2018)

Article Genetics & Heredity

MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association

Limin Jiang et al.

FRONTIERS IN GENETICS (2018)

Article Biochemistry & Molecular Biology

dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers

Zhen Yang et al.

NUCLEIC ACIDS RESEARCH (2017)

Article Biochemical Research Methods

Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning

Jiawei Luo et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2017)

Article Computer Science, Interdisciplinary Applications

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction

Xing Chen et al.

JOURNAL OF BIOMEDICAL INFORMATICS (2017)

Article Biochemical Research Methods

Prediction of potential disease-associated microRNAs based on random walk

Ping Xuan et al.

BIOINFORMATICS (2015)

Article Biochemical Research Methods

Protein-driven inference of miRNA-disease associations

Soren Mork et al.

BIOINFORMATICS (2014)

Article Genetics & Heredity

Similarity-based methods for potential human microRNA-disease association prediction

Hailin Chen et al.

BMC MEDICAL GENOMICS (2013)

Article Biochemistry & Molecular Biology

Expression patterns of microRNAs associated with CML phases and their disease related targets

Katerina Machova Polakova et al.

MOLECULAR CANCER (2011)

Review Biochemistry & Molecular Biology

Origins and Mechanisms of miRNAs and siRNAs

Richard W. Carthew et al.

Article Multidisciplinary Sciences

An Analysis of Human MicroRNA and Disease Associations

Ming Lu et al.

PLOS ONE (2008)

Review Biochemistry & Molecular Biology

MicroRNAs: Genomics, biogenesis, mechanism, and function

DP Bartel

Article Multidisciplinary Sciences

The functions of animal microRNAs

V Ambros

NATURE (2004)