Graph neural networks and their current applications in bioinformatics

XM Zhang, L Liang, L Liu, MJ Tang - Frontiers in genetics, 2021 - frontiersin.org
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field

W Wang, L Zhang, J Sun, Q Zhao… - Briefings in …, 2022 - academic.oup.com
Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding
RNAs (ncRNAs), their interaction plays an important regulatory role in many biological …

A deep learning method for predicting metabolite–disease associations via graph neural network

F Sun, J Sun, Q Zhao - Briefings in bioinformatics, 2022 - academic.oup.com
Metabolism is the process by which an organism continuously replaces old substances with
new substances. It plays an important role in maintaining human life, body growth and …

Predicting drug–disease associations through layer attention graph convolutional network

Z Yu, F Huang, X Zhao, W **ao… - Briefings in …, 2021 - academic.oup.com
Background: Determining drug–disease associations is an integral part in the process of
drug development. However, the identification of drug–disease associations through wet …

MiRNA-based therapies for lung cancer: Opportunities and challenges?

H Yang, Y Liu, L Chen, J Zhao, M Guo, X Zhao, Z Wen… - Biomolecules, 2023 - mdpi.com
Lung cancer is a commonly diagnosed cancer and the leading cause of cancer-related
deaths, posing a serious health risk. Despite new advances in immune checkpoint and …

HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks

BW Zhao, L Hu, ZH You, L Wang… - Briefings in …, 2022 - academic.oup.com
Identifying new indications for drugs plays an essential role at many phases of drug
research and development. Computational methods are regarded as an effective way to …

Drug repositioning based on the heterogeneous information fusion graph convolutional network

L Cai, C Lu, J Xu, Y Meng, P Wang, X Fu… - Briefings in …, 2021 - academic.oup.com
In silico reuse of old drugs (also known as drug repositioning) to treat common and rare
diseases is increasingly becoming an attractive proposition because it involves the use of de …

Network learning for biomarker discovery

Y Ding, M Fu, P Luo, FX Wu - International Journal of Network Dynamics …, 2023 - sciltp.com
Everything is connected and thus networks are instrumental in not only modeling complex
systems with many components, but also accommodating knowledge about their …

Predicting human microbe–drug associations via graph convolutional network with conditional random field

Y Long, M Wu, CK Kwoh, J Luo, X Li - Bioinformatics, 2020 - academic.oup.com
Motivation Human microbes play critical roles in drug development and precision medicine.
How to systematically understand the complex interaction mechanism between human …

GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning

J Wei, L Zhuo, Z Zhou, X Lian, X Fu… - Briefings in …, 2023 - academic.oup.com
Studies have shown that the mechanism of action of many drugs is related to miRNA. In-
depth research on the relationship between miRNA and drugs can provide theoretical …