Recommender systems in the healthcare domain: state-of-the-art and research issues
Nowadays, a vast amount of clinical data scattered across different sites on the Internet
hinders users from finding helpful information for their well-being improvement. Besides, the …
hinders users from finding helpful information for their well-being improvement. Besides, the …
Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches
B Güvenç Paltun, H Mamitsuka… - Briefings in …, 2021 - academic.oup.com
Predicting the response of cancer cell lines to specific drugs is one of the central problems in
personalized medicine, where the cell lines show diverse characteristics. Researchers have …
personalized medicine, where the cell lines show diverse characteristics. Researchers have …
Identification of drug-side effect association via multiple information integration with centered kernel alignment
In medicine research, drug discovery aims to develop a drug to patients who will benefit from
it and try to avoid some side effects. However, the tradition experiment is time consuming …
it and try to avoid some side effects. However, the tradition experiment is time consuming …
SuperPred: update on drug classification and target prediction
J Nickel, BO Gohlke, J Erehman… - Nucleic acids …, 2014 - academic.oup.com
The SuperPred web server connects chemical similarity of drug-like compounds with
molecular targets and the therapeutic approach based on the similar property principle …
molecular targets and the therapeutic approach based on the similar property principle …
Meta learning with graph attention networks for low-data drug discovery
Finding candidate molecules with favorable pharmacological activity, low toxicity, and
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …
Predicting the frequencies of drug side effects
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in
humans. Currently, frequencies are experimentally determined in randomised controlled …
humans. Currently, frequencies are experimentally determined in randomised controlled …
Predicting drug side effects by multi-label learning and ensemble learning
W Zhang, F Liu, L Luo, J Zhang - BMC bioinformatics, 2015 - Springer
Background Predicting drug side effects is an important topic in the drug discovery. Although
several machine learning methods have been proposed to predict side effects, there is still …
several machine learning methods have been proposed to predict side effects, there is still …
Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
Unknown adverse reactions to drugs available on the market present a significant health risk
and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning …
and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning …
Predicting drug side effects with compact integration of heterogeneous networks
X Zhao, L Chen, ZH Guo, T Liu - Current Bioinformatics, 2019 - ingentaconnect.com
Background: The side effects of drugs are not only harmful to humans but also the major
reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical …
reasons for withdrawing approved drugs, bringing greater risks for pharmaceutical …
Predicting potential side effects of drugs by recommender methods and ensemble learning
Drugs provide help and promise for human health, but they usually come with side effects.
Predicting side effects of drugs is a critical issue for the drug discovery. Although several …
Predicting side effects of drugs is a critical issue for the drug discovery. Although several …