Biomedical data and computational models for drug repositioning: a comprehensive review
Drug repositioning can drastically decrease the cost and duration taken by traditional drug
research and development while avoiding the occurrence of unforeseen adverse events …
research and development while avoiding the occurrence of unforeseen adverse events …
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 …
Drug–drug similarity measure and its applications
L Huang, H Luo, S Li, FX Wu… - Briefings in …, 2021 - academic.oup.com
Drug similarities play an important role in modern biology and medicine, as they help
scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that …
scientists gain deep insights into drugs' therapeutic mechanisms and conduct wet labs that …
KG-Predict: A knowledge graph computational framework for drug repurposing
The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data
has offered unprecedented opportunities for drug discovery including drug repurposing …
has offered unprecedented opportunities for drug discovery including drug repurposing …
Predicting drug–drug interactions by graph convolutional network with multi-kernel
Drug repositioning is proposed to find novel usages for existing drugs. Among many types of
drug repositioning approaches, predicting drug–drug interactions (DDIs) helps explore the …
drug repositioning approaches, predicting drug–drug interactions (DDIs) helps explore the …
DeepDSC: a deep learning method to predict drug sensitivity of cancer cell lines
High-throughput screening technologies have provided a large amount of drug sensitivity
data for a panel of cancer cell lines and hundreds of compounds. Computational …
data for a panel of cancer cell lines and hundreds of compounds. Computational …
Feature selection for microarray data classification using hybrid information gain and a modified binary krill herd algorithm
G Zhang, J Hou, J Wang, C Yan, J Luo - … Sciences: Computational Life …, 2020 - Springer
Due to the presence of irrelevant or redundant data in microarray datasets, capturing
potential patterns accurately and directly via existing models is difficult. Feature selection …
potential patterns accurately and directly via existing models is difficult. Feature selection …
Discovery of potent SARS-CoV-2 inhibitors from approved antiviral drugs via docking and virtual screening
Background: Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten
patients, societies and healthcare systems around the world. There is an urgent need to …
patients, societies and healthcare systems around the world. There is an urgent need to …
SANE: a sequence combined attentive network embedding model for COVID-19 drug repositioning
The COVID-19 has now spread all over the world and causes a huge burden for public
health and world economy. Drug repositioning has become a promising treatment strategy …
health and world economy. Drug repositioning has become a promising treatment strategy …
A network-based drug repurposing method via non-negative matrix factorization
Motivation Drug repurposing is a potential alternative to the traditional drug discovery
process. Drug repurposing can be formulated as a recommender system that recommends …
process. Drug repurposing can be formulated as a recommender system that recommends …