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

A novel graph attention adversarial network for predicting disease-related associations.

Methods (San Diego, Calif.) - 1 Jul 2020

Zhang Jinli, Jiang Zongli, Hu Xiaohua, Song Bo

Abstract excerpt

Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human diseases. However, the amount of verified disease-related RNAs is still little while many of their biological experiments are very time-consuming and...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Databases, Genetic
Share to Datasets as Topic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Gene Regulatory Networks

Share to MicroRNAs

Share to Predictive Value of Tests

Share to RNA, Long Noncoding

Source

Identifiers

PMID: 32446956
DOI: 10.1016/j.ymeth.2020.05.010

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

A novel graph attention adversarial network for predicting disease-related associations.PMID 32446956Read ↗

Predicting LncRNA-Disease Association Based on Generative Adversarial Network.Semantic proximity Read ↗

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

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

HiGLDP: a hierarchical graph neural network for predicting lncRNA-disease associations through multi-omic integration.Semantic proximity Read ↗

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association predictionSemantic proximity Read ↗

iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations.Semantic proximity Read ↗

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

Different Graph-Level Attention Based on Multi-Scale for Predicting lncRNA-Disease Associations.Semantic proximity Read ↗

Predicting miRNA-disease associations based on graph attention network with multi-source information.Semantic proximity Read ↗

MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.Semantic proximity Read ↗

Predicting miRNA-disease associations based on graph random propagation network and attention network.Semantic proximity Read ↗

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.Semantic proximity Read ↗

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