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MicroRNAs

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Predicting miRNA-disease associations using an ensemble learning framework with resampling method.

Briefings in bioinformatics
MOTIVATION: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and tr...

LR-GNN: a graph neural network based on link representation for predicting molecular associations.

Briefings in bioinformatics
In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discov...

SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations.

Briefings in bioinformatics
MiRNAs are a class of small non-coding RNA molecules that play an important role in many biological processes, and determining miRNA-disease associations can benefit drug development and clinical diagnosis. Although great efforts have been made to de...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNA (Shariqah, United Arab Emirates)
MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated with many diseases. Artificial Intelligence (AI) is an evolving cluster of interrelated fields, with ma...

miRNAs expression pattern and machine learning models elucidate risk for gastric GIST.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Gatrointestinal stromal tumors (GISTs) are the main mesenchymal tumors found in the gastrointestinal system. GISTs clinical phenotypes differ significantly and their molecular basis is not yet completely known. microRNAs (miRNAs) have bee...

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

Briefings in bioinformatics
MOTIVATION: Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic m...

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

Briefings in bioinformatics
MOTIVATION: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of...

NMCMDA: neural multicategory MiRNA-disease association prediction.

Briefings in bioinformatics
MOTIVATION: There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an imp...

GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.

Briefings in bioinformatics
Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification appr...

A graph auto-encoder model for miRNA-disease associations prediction.

Briefings in bioinformatics
Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and ...