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

Different Graph-Level Attention Based on Multi-Scale for Predicting lncRNA-Disease Associations.

IEEE transactions on computational biology and bioinformatics - 1 Jan 2026

Ma Haitao, Hu Zhenkang, Tu Jiale, Zhou Xuxian, Zhao Yuhai, Yu Changyong

Abstract excerpt

The relationship between long non-coding RNAs (lncRNAs) and diseases is crucial for understanding biological processes, as well as the onset, progression, prevention, and treatment of diseases. Accurate prediction of associations between lncRNAs and diseases holds significant potential, offering new insights for biological research and identifying novel therapeutic targets for clinical applications. These...

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

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Predisposition to Disease

Share to RNA, Long Noncoding

Source

Identifiers

PMID: 41632643
DOI: 10.1109/TCBBIO.2026.3660679

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Different Graph-Level Attention Based on Multi-Scale for Predicting lncRNA-Disease Associations.PMID 41632643Read ↗

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

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

DSGCNLDA: A Multi-view Learning Model with DualScope Attention for lncRNA-Disease Association Prediction.Semantic proximity Read ↗

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

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

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

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

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.Semantic proximity Read ↗

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

Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs.Semantic proximity Read ↗

Multi-Level Attention Graph Neural Network for Clinically Interpretable Pathway-Level Biomarkers DiscoverySemantic proximity Read ↗

Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosisSemantic proximity Read ↗

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