SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs

K Zheng, XL Zhang, L Wang, ZH You… - Briefings in …, 2023 - academic.oup.com
Abstract piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment
as novel biomarkers due to its abnormal expression in various cancers. However, the …

MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning

W Liu, T Tang, X Lu, X Fu, Y Yang… - Briefings in …, 2023 - academic.oup.com
Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying
the associations between human diseases and circRNA can help in disease prevention …

Ichrom-deep: an attention-based deep learning model for identifying chromatin interactions

P Zhang, H Wu - IEEE Journal of Biomedical and Health …, 2023 - ieeexplore.ieee.org
Identification of chromatin interactions is crucial for advancing our knowledge of gene
regulation. However, due to the limitations of high-throughput experimental techniques …

AMDECDA: attention mechanism combined with data ensemble strategy for predicting CircRNA-disease association

L Wang, L Wong, ZH You… - IEEE Transactions on Big …, 2023 - ieeexplore.ieee.org
Accumulating evidence from recent research reveals that circRNA is tightly bound to human
complex disease and plays an important regulatory role in disease progression. Identifying …

Community graph convolution neural network for alzheimer's disease classification and pathogenetic factors identification

XA Bi, K Chen, S Jiang, S Luo, W Zhou… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
As a complex neural network system, the brain regions and genes collaborate to effectively
store and transmit information. We abstract the collaboration correlations as the brain region …

Explainable and programmable hypergraph convolutional network for imaging genetics data fusion

X Bi, S Luo, S Jiang, Y Wang, Z Xing, L Xu - Information Fusion, 2023 - Elsevier
Integrating multi-view information to gain a new understanding of complex disease like
Alzheimer's disease (AD) has great clinical value. Hypergraphs have unique advantages in …

GSLCDA: an unsupervised deep graph structure learning method for predicting CircRNA-disease association

L Wang, ZW Li, ZH You, DS Huang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological
processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated …

DeepCMI: a graph-based model for accurate prediction of circRNA–miRNA interactions with multiple information

YC Li, ZH You, CQ Yu, L Wang, L Hu… - Briefings in …, 2024 - academic.oup.com
Recently, the role of competing endogenous RNAs in regulating gene expression through
the interaction of microRNAs has been closely associated with the expression of circular …

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks

XF Wang, CQ Yu, ZH You, Y Wang, L Huang, Y Qiao… - BMC …, 2024 - Springer
Abstract Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important
model for the regulation of biological processes by non-coding RNA (ncRNA), which …

[HTML][HTML] CircRNA-based therapeutics: Current opinions and clinical potential

H Liu, X Yao, Y Zhou, L Chen - The Innovation Medicine, 2024 - the-innovation.org
Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules that
perform diverse roles in various cellular processes and have been implicated in many …