Heterogeneous biomedical entity representation learning for gene–disease association prediction

Z Meng, S Liu, S Liang, B Jani… - Briefings in …, 2024 - academic.oup.com
Understanding the genetic basis of disease is a fundamental aspect of medical research, as
genes are the classic units of heredity and play a crucial role in biological function …

TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network

S Long, X Tang, X Si, T Kong, Y Zhu, C Wang… - Communications …, 2024 - nature.com
The identification of miRNA-disease associations is crucial for early disease prevention and
treatment. However, it is still a computational challenge to accurately predict such …

RNA Sequence Analysis Landscape: A Comprehensive Review of Task Types, Databases, Datasets, Word Embedding Methods, and Language Models

MN Asim, MA Ibrahim, T Asif, A Dengel - Heliyon, 2025 - cell.com
Deciphering information of RNA sequences reveals their diverse roles in living organisms,
including gene regulation and protein synthesis. Aberrations in RNA sequence such as …

[HTML][HTML] PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path

L Chen, X Zhao - Mathematical Biosciences and Engineering, 2023 - aimspress.com
Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play
important regulatory roles in human diseases through interactions with related microRNAs …

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

S Gao, Z Kuang, T Duan, L Deng - Frontiers in Medicine, 2023 - frontiersin.org
Numerous studies have shown that miRNAs play a crucial role in the investigation of
complex human diseases. Identifying the connection between miRNAs and diseases is …

DAEMDA: a method with dual-channel attention encoding for miRNA–disease association prediction

B Dong, W Sun, D Xu, G Wang, T Zhang - Biomolecules, 2023 - mdpi.com
A growing number of studies have shown that aberrant microRNA (miRNA) expression is
closely associated with the evolution and development of various complex human diseases …

Causal enhanced drug–target interaction prediction based on graph generation and multi-source information fusion

G Qiao, G Wang, Y Li - Bioinformatics, 2024 - academic.oup.com
Motivation The prediction of drug–target interaction is a vital task in the biomedical field,
aiding in the discovery of potential molecular targets of drugs and the development of …

[HTML][HTML] Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

S Guan, G Wang - Artificial Intelligence Chemistry, 2024 - Elsevier
Abstract Drug Research and Development (R&D) is a complex and difficult process, and
current drug R&D faces the challenges of long time span, high investment, and high failure …

[HTML][HTML] Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction

H Bai, S Lu, T Zhang, H Cui, T Nakaguchi, P Xuan - Iscience, 2024 - cell.com
Identifying the side effects related to drugs is beneficial for reducing the risk of drug
development failure and saving the drug development cost. We proposed a graph reasoning …

Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure

R Xu, D Li, W Yang, G Wang, Y Li - Bioinformatics, 2024 - academic.oup.com
Motivation Recent advancements in high-throughput sequencing technology have
significantly increased the focus on non-coding RNA (ncRNA) research within the life …