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

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.

IEEE/ACM transactions on computational biology and bioinformatics - 1 Jan 2000

Yan Cheng, Duan Guihua

Abstract excerpt

Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. Therefore, many computational methods have been developed to identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment. In this study, we also introduced a new...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology
Share to Genetic Predisposition to Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to MicroRNAs

Share to Neural Networks, Computer

Source

Identifiers

PMID: 38358864
DOI: 10.1109/TCBB.2024.3366175

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

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

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.PMID 38358864Read ↗

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

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.Semantic proximity Read ↗

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

A method for miRNA diffusion association prediction using machine learning decoding of multi-level heterogeneous graph Transformer encoded representations.Semantic proximity Read ↗

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.Semantic proximity Read ↗

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.Semantic proximity Read ↗

PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.Semantic proximity Read ↗

Identifying human microRNA-disease associations by a new diffusion-based method.Semantic proximity Read ↗

Prediction of miRNA-disease associations by neural network-based deep matrix factorization.Semantic proximity Read ↗

A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations.Semantic proximity Read ↗

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.Semantic proximity Read ↗

Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.Semantic proximity Read ↗

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