Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer

C Kulkarni, A Quraishi, M Raparthi, M Shabaz… - BMC Medical Informatics …, 2024 - Springer
Emerging from the convergence of digital twin technology and the metaverse, consumer
health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with …

Mshganmda: Meta-subgraphs heterogeneous graph attention network for mirna-disease association prediction

S Wang, F Wang, S Qiao, Y Zhuang… - IEEE journal of …, 2022 - ieeexplore.ieee.org
MicroRNAs (miRNAs) influence several biological processes involved in human disease.
Biological experiments for verifying the association between miRNA and disease are always …

Gcnpca: miRNA-disease associations prediction algorithm based on graph convolutional neural networks

J Liu, Z Kuang, L Deng - IEEE/ACM transactions on …, 2022 - ieeexplore.ieee.org
A growing number of studies have confirmed the important role of microRNAs (miRNAs) in
human diseases and the aberrant expression of miRNAs affects the onset and progression …

TARSL: Triple-attention cross-network representation learning to predict synthetic lethality for anti-cancer drug discovery

J Li, X Lu, K Jiang, D Tang, B Ning… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Cancer is a multifaceted disease that results from co-mutations of multi biological molecules.
A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic …

DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

P Wei, Q Wang, Z Gao, R Cao, C Zheng - Frontiers of Computer Science, 2024 - Springer
MicroRNAs (miRNAs) are closely related to numerous complex human diseases, therefore,
exploring miRNA-disease associations (MDAs) can help people gain a better understanding …

MAGCN: A multiple attention graph convolution networks for predicting synthetic lethality

X Lu, G Chen, J Li, X Hu, F Sun - IEEE/ACM Transactions on …, 2022 - ieeexplore.ieee.org
Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery.
Computational approaches to identify synthetic lethality genes have become an effective …

A MiRNA-Disease Association Prediction Method Integrating Graph Matrix Factorization With L similarity Constraint And Network Projection Fusion

G Xie, W Li, G Gu, Z Lin, D Li - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Discovering miRNAs associated with diseases can contribute to understanding the
pathogenesis and treatment strategies of diseases. In the commonly used graph regularized …

Multiple Heterogeneous Networks Representation with Latent Space for Synthetic Lethality Prediction

X Hu, H Yi, H Cheng, Y Zhao, D Zhang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Computational synthetic lethality (SL) method has become a promising strategy to identify
SL gene pairs for targeted cancer therapy and cancer medicine development. Feature …

Latent space feature representation on multiple biological network for synthetic lethality interaction prediction

J Li, X Lu, K Jiang, D Tang, F Sun… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Computational methods to discover potential synthetic lethality (SL) pairs has become a
promising strategy for targeted cancer therapy and cancer medicine development. Despite …

ISFMDA: learning interactions of selected features-based method for predicting potential MicroRNA-disease associations

X Chen, Z Jiang - Journal of Computational Biology, 2021 - liebertpub.com
Prediction of potential microRNA-disease associations is one of the important tasks in
computational biology fields. Mining more sophisticated features can improve the …