[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 …
Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning
Background Development of new drugs is a time-consuming and costly process, and the
cost is still increasing in recent years. However, the number of drugs approved by FDA every …
cost is still increasing in recent years. However, the number of drugs approved by FDA every …
A review of recent developments and progress in computational drug repositioning
W Shi, X Chen, L Deng - Current Pharmaceutical Design, 2020 - ingentaconnect.com
Computational drug repositioning is an efficient approach towards discovering new
indications for existing drugs. In recent years, with the accumulation of online health-related …
indications for existing drugs. In recent years, with the accumulation of online health-related …
Learning representations to predict intermolecular interactions on large-scale heterogeneous molecular association network
Molecular components that are functionally interdependent in human cells constitute
molecular association networks. Disease can be caused by disturbance of multiple …
molecular association networks. Disease can be caused by disturbance of multiple …
Data integration using tensor decomposition for the prediction of miRNA-disease associations
Dysfunction of miRNAs has an important relationship with diseases by impacting their target
genes. Identifying disease-related miRNAs is of great significance to prevent and treat …
genes. Identifying disease-related miRNAs is of great significance to prevent and treat …
Learning data-driven drug-target-disease interaction via neural tensor network
Precise medicine recommendations provide more effective treatments and cause fewer drug
side effects. A key step is to understand the mechanistic relationships among drugs, targets …
side effects. A key step is to understand the mechanistic relationships among drugs, targets …
Learning multi-scale heterogeneous representations and global topology for drug-target interaction prediction
Identification of interactions between drugs and target proteins plays a critical role not only in
drug discovery but also in drug repositioning. Deep integration of inter-connections and intra …
drug discovery but also in drug repositioning. Deep integration of inter-connections and intra …
miRCom: tensor completion integrating multi-view information to deduce the potential disease-related miRNA-miRNA pairs
MicroRNAs (miRNAs) are consistently capable of regulating gene expression synergistically
in a combination mode and play a key role in various biological processes associated with …
in a combination mode and play a key role in various biological processes associated with …
ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction
Motivation Effective computational methods to predict drug–protein interactions (DPIs) are
vital for drug discovery in reducing the time and cost of drug development. Recent DPI …
vital for drug discovery in reducing the time and cost of drug development. Recent DPI …
NTD-DR: Nonnegative tensor decomposition for drug repositioning
Computational drug repositioning aims to identify potential applications of existing drugs for
the treatment of diseases for which they were not designed. This approach can considerably …
the treatment of diseases for which they were not designed. This approach can considerably …