Predicting drug–disease associations through layer attention graph convolutional network
Background: Determining drug–disease associations is an integral part in the process of
drug development. However, the identification of drug–disease associations through wet …
drug development. However, the identification of drug–disease associations through wet …
Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models
L Wang, Y Tan, X Yang, L Kuang… - Briefings in …, 2022 - academic.oup.com
In recent years, with the rapid development of techniques in bioinformatics and life science,
a considerable quantity of biomedical data has been accumulated, based on which …
a considerable quantity of biomedical data has been accumulated, based on which …
Introduction to focus issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics
Machine learning (ML), a subset of artificial intelligence, refers to methods that have the
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …
ability to “learn” from experience, enabling them to carry out designated tasks. Examples of …
REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction
Computational drug repositioning is an effective way to find new indications for existing
drugs, thus can accelerate drug development and reduce experimental costs. Recently …
drugs, thus can accelerate drug development and reduce experimental costs. Recently …
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest
QW Wu, JF Xia, JC Ni, CH Zheng - Briefings in bioinformatics, 2021 - academic.oup.com
Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new
biomarkers for prevention, diagnosis and treatment of complex human diseases. In this …
biomarkers for prevention, diagnosis and treatment of complex human diseases. In this …
Partner-specific drug repositioning approach based on graph convolutional network
Drug repositioning identifies novel therapeutic potentials for existing drugs and is
considered an attractive approach due to the opportunity for reduced development timelines …
considered an attractive approach due to the opportunity for reduced development timelines …
[HTML][HTML] An explainable framework for drug repositioning from disease information network
Exploring efficient and high-accuracy computational drug repositioning methods has
become a popular and attractive topic in drug development. This technology can …
become a popular and attractive topic in drug development. This technology can …
PrGeFNE: predicting disease-related genes by fast network embedding
Identifying disease-related genes is of importance for understanding of molecule
mechanisms of diseases, as well as diagnosis and treatment of diseases. Many …
mechanisms of diseases, as well as diagnosis and treatment of diseases. Many …
Computational approaches for predicting drug-disease associations: a comprehensive review
Z Huang, Z Xiao, C Ao, L Guan, L Yu - Frontiers of Computer Science, 2025 - Springer
In recent decades, traditional drug research and development have been facing challenges
such as high cost, long timelines, and high risks. To address these issues, many …
such as high cost, long timelines, and high risks. To address these issues, many …
A Multimodal Framework for Improving in Silico Drug Repositioning With the Prior Knowledge From Knowledge Graphs
Drug repositioning/repurposing is a very important approach towards identifying novel
treatments for diseases in drug discovery. Recently, large-scale biological datasets are …
treatments for diseases in drug discovery. Recently, large-scale biological datasets are …