Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism

T Wang, J Sun, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big
concern during drug development in the pharmaceutical industry. Failure or inhibition of …

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 metabolite–disease associations based on auto-encoder and non-negative matrix factorization

H Gao, J Sun, Y Wang, Y Lu, L Liu… - Briefings in …, 2023 - academic.oup.com
Metabolism refers to a series of orderly chemical reactions used to maintain life activities in
organisms. In healthy individuals, metabolism remains within a normal range. However …

scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention

R Meng, S Yin, J Sun, H Hu, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …

Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder

W Liu, H Lin, L Huang, L Peng, T Tang… - Briefings in …, 2022 - academic.oup.com
Increasing evidences show that the occurrence of human complex diseases is closely
related to microRNA (miRNA) variation and imbalance. For this reason, predicting disease …

DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction

Z Chen, L Zhang, J Sun, R Meng… - Journal of cellular and …, 2023 - Wiley Online Library
The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity
testing of new compounds is very necessary before being put on the market. Currently, many …

Single-cell sequencing: expansion, integration and translation

X Dai, L Cai, F He - Briefings in Functional Genomics, 2022 - academic.oup.com
With the rapid advancement in sequencing technologies, the concept of omics has
revolutionized our understanding of cellular behaviors. Conventional omics investigation …

[HTML][HTML] SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction

X Li, P Han, G Wang, W Chen, S Wang, T Song - BMC genomics, 2022 - Springer
Abstract Background Protein-protein interactions (PPIs) dominate intracellular molecules to
perform a series of tasks such as transcriptional regulation, information transduction, and …

[HTML][HTML] Hierarchical graph attention network for miRNA-disease association prediction

Z Li, T Zhong, D Huang, ZH You, R Nie - Molecular Therapy, 2022 - cell.com
Many biological studies show that the mutation and abnormal expression of microRNAs
(miRNAs) could cause a variety of diseases. As an important biomarker for disease …