Graph pooling in graph neural networks: Methods and their applications in omics studies
Y Wang, W Hou, N Sheng, Z Zhao, J Liu… - Artificial Intelligence …, 2024 - Springer
Graph neural networks (GNNs) process the graph-structured data using neural networks
and have proven successful in various graph processing tasks. Currently, graph pooling …
and have proven successful in various graph processing tasks. Currently, graph pooling …
BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks
Abstract Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important
model for the regulation of biological processes by non-coding RNA (ncRNA), which …
model for the regulation of biological processes by non-coding RNA (ncRNA), which …
Unsupervised multi-view graph representation learning with dual weight-net
Unsupervised multi-view graph representation learning (UMGRL) aims to capture the
complex relationships in the multi-view graph without human annotations, so it has been …
complex relationships in the multi-view graph without human annotations, so it has been …
GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations
D Yao, B Li, X Zhan, X Zhan, L Yu - BMC bioinformatics, 2024 - Springer
Background A growing body of researches indicate that the disrupted expression of long
non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective …
non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective …
RNA Sequence Analysis Landscape: A Comprehensive Review of Task Types, Databases, Datasets, Word Embedding Methods, and Language Models
Deciphering information of RNA sequences reveals their diverse roles in living organisms,
including gene regulation and protein synthesis. Aberrations in RNA sequence such as …
including gene regulation and protein synthesis. Aberrations in RNA sequence such as …
Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases
Exploring potential association between long non-coding RNAs (lncRNAs), microRNAs
(miRNAs) and diseases is an essential part of prevention, diagnosis and treatment of …
(miRNAs) and diseases is an essential part of prevention, diagnosis and treatment of …
X-lda: an interpretable and knowledge-informed heterogeneous graph learning framework for lncrna-disease association prediction
The identification of disease-related long noncoding RNAs (lncRNAs) is beneficial to
unravel the intricacies of gene expression regulation and epigenetic signatures …
unravel the intricacies of gene expression regulation and epigenetic signatures …
LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks
B Zhang, H Wang, C Ma, H Huang, Z Fang, J Qu - BMC bioinformatics, 2024 - Springer
Abstract Background Long non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a
variety of complex human diseases, and it is crucial to establish a method to efficiently …
variety of complex human diseases, and it is crucial to establish a method to efficiently …
Predicting noncoding RNA and disease associations using multigraph contrastive learning
SL Sun, YY Jiang, JP Yang, YH Xiu, A Bilal… - Scientific Reports, 2025 - nature.com
MiRNAs and lncRNAs are two essential noncoding RNAs. Predicting associations between
noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis …
noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis …
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers
XF Wang, L Huang, Y Wang, RC Guan… - Briefings in …, 2024 - academic.oup.com
Identifying potential cancer biomarkers is a key task in biomedical research, providing a
promising avenue for the diagnosis and treatment of human tumors and cancers. In recent …
promising avenue for the diagnosis and treatment of human tumors and cancers. In recent …