Article

AMPGLDA: Predicting LncRNA-Disease Associations Based on Adaptive Meta-Path Generation and Multi-Layer Perceptron

2024-06-03

Abstract excerpt

Increased evidence suggests that long non-coding RNA (lncRNA) holds a vital position in intricate human diseases. Nonetheless, the current pool of identified lncRNA linked to diseases remains restricted. Hence, the scientific community emphasizes the need for a reliable and cost-effective computational approach to predict the probable correlations between lncRNA and diseases. It would facilitate the exploration of...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 62c8277e-b5cf-5745-aa7b-a4ecc4557abb
DOI: 10.1101/2024.06.02.596998

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

AMPGLDA: Predicting LncRNA-Disease Associations Based on Adaptive Meta-Path Generation and Multi-Layer PerceptronDOI 10.1101/2024.06.02.596998Read

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.Semantic proximity Read

LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction.Semantic proximity Read

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

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

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

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-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

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

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

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

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