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

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

Journal of chemical information and modeling - 26 Aug 2024

Xuan Ping, Wang Wei, Cui Hui, Wang Shuai, Nakaguchi Toshiya, Zhang Tiangang

Abstract excerpt

Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of the causes and progression of these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting the topological structure and node attributes within graphs. However, these methods did not prioritize the target lncRNA and disease nodes over...

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 Genetic Predisposition to Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to RNA, Long Noncoding

Source

Identifiers

PMID: 39112431
DOI: 10.1021/acs.jcim.4c00652

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

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.PMID 39112431Read ↗

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

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.Semantic proximity Read ↗

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

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 ↗

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

iDRKAN: Interpretable miRNA-Disease Association Prediction Based on Dual-Graph Representation Learning and Kolmogorov-Arnold Network.Semantic proximity Read ↗

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

Predicting noncoding RNA and disease associations using multigraph contrastive learning.Semantic proximity Read ↗

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.Semantic proximity Read ↗

Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood informationSemantic proximity Read ↗

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

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