Circular RNAs and complex diseases: from experimental results to computational models

CC Wang, CD Han, Q Zhao, X Chen - Briefings in bioinformatics, 2021 - academic.oup.com
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules
with a variety of biological functions. Studies have shown that circRNAs are involved in a …

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 …

MK-FSVM-SVDD: a multiple kernel-based fuzzy SVM model for predicting DNA-binding proteins via support vector data description

Y Zou, H Wu, X Guo, L Peng, Y Ding… - Current …, 2021 - ingentaconnect.com
Background: Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive. Objective: In recent years, the rise of …

Identification of drug–target interactions via dual laplacian regularized least squares with multiple kernel fusion

Y Ding, J Tang, F Guo - Knowledge-Based Systems, 2020 - Elsevier
Abstract Detection of Drug–Target Interactions (DTIs) is the time-consuming and laborious
experiment via biochemical approaches. Machine learning based methods have been …

[HTML][HTML] Identify DNA-binding proteins through the extreme gradient boosting algorithm

Z Zhao, W Yang, Y Zhai, Y Liang, Y Zhao - Frontiers in Genetics, 2022 - frontiersin.org
The exploration of DNA-binding proteins (DBPs) is an important aspect of studying
biological life activities. Research on life activities requires the support of scientific research …

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 …

[HTML][HTML] Predicting lncRNA–miRNA interactions based on interactome network and graphlet interaction

L Zhang, T Liu, H Chen, Q Zhao, H Liu - Genomics, 2021 - Elsevier
In the development and treatment of many human diseases, the regulatory roles between
lncRNAs and miRNAs are important, but much remains unknown about them; moreover …

[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 …