Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

PLoS computational biology - 1 Dec 2021

Li Lei, Wang Yu-Tian, Ji Cun-Mei, Zheng Chun-Hou, Ni Jian-Cheng, Su Yan-Sen

Abstract excerpt

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Area Under Curve
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to MicroRNAs

Share to Models, Genetic

Share to Neoplasms

Share to Neural Networks, Computer

Source

Open in PubMed Open full text

Identifiers

PMID: 34890410
DOI: 10.1371/journal.pcbi.1009655
PMCID: PMC8694430

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.PMID 34890410Read ↗

FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.Semantic proximity Read ↗

Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network.Semantic proximity Read ↗

HybridGNN: a graph neural network approach for human miRNA-disease association prediction.Semantic proximity Read ↗

A learning-based framework for miRNA-disease association identification using neural networksSemantic proximity Read ↗

GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction.Semantic proximity Read ↗

Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.Semantic proximity Read ↗

NMCMDA: neural multicategory MiRNA-disease association prediction.Semantic proximity Read ↗

AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification.Semantic proximity Read ↗

Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder.Semantic proximity Read ↗

AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network.Semantic proximity Read ↗

Adaptive deep propagation graph neural network for predicting miRNA–disease associationsSemantic proximity Read ↗

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association predictionSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.