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 …

Drug–drug interaction prediction: databases, web servers and computational models

Y Zhao, J Yin, L Zhang, Y Zhang… - Briefings in …, 2024 - academic.oup.com
In clinical treatment, two or more drugs (ie drug combination) are simultaneously or
successively used for therapy with the purpose of primarily enhancing the therapeutic …

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 …

Drug design and disease diagnosis: the potential of deep learning models in biology

S Sreeraman, MP Kannan… - Current …, 2023 - ingentaconnect.com
Early prediction and detection enable reduced transmission of human diseases and provide
healthcare professionals ample time to make subsequent diagnoses and treatment …

SMAP: Similarity-based matrix factorization framework for inferring miRNA-disease association

J Ha - Knowledge-Based Systems, 2023 - Elsevier
Background: Based on increasing evidence, microRNAs (miRNAs) play significant roles in
various complex human diseases. Therefore, identifying the disease-related miRNAs could …

Predicting lncRNA–disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation

GB **e, RB Chen, ZY Lin, GS Gu, JR Yu… - Briefings in …, 2023 - academic.oup.com
Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to
several human diseases, providing new opportunities for their use in detection and therapy …

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

X Wu, C Yuan, J Pan, Y Zhou, X Pan, J Kang, L Ren… - Scientific Reports, 2024 - nature.com
Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular
steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid …

MSGCL: inferring miRNA–disease associations based on multi-view self-supervised graph structure contrastive learning

X Ruan, C Jiang, P Lin, Y Lin, J Liu… - Briefings in …, 2023 - academic.oup.com
Potential miRNA–disease associations (MDA) play an important role in the discovery of
complex human disease etiology. Therefore, MDA prediction is an attractive research topic …

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks

XF Wang, CQ Yu, ZH You, LP Li… - Briefings in …, 2023 - academic.oup.com
Motivation A large number of studies have shown that circular RNA (circRNA) affects
biological processes by competitively binding miRNA, providing a new perspective for the …

A survey of deep learning for detecting miRNA-disease associations: databases, computational methods, challenges, and future directions

N Sheng, X **e, Y Wang, L Huang… - IEEE/ACM …, 2024 - ieeexplore.ieee.org
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role
in the occurrence and development of various diseases. Identifying the potential miRNA …