Recommender systems in the healthcare domain: state-of-the-art and research issues

TNT Tran, A Felfernig, C Trattner… - Journal of Intelligent …, 2021 - Springer
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 …

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 …

Identification of drug-side effect association via multiple information integration with centered kernel alignment

Y Ding, J Tang, F Guo - Neurocomputing, 2019 - Elsevier
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 …

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 …

Meta learning with graph attention networks for low-data drug discovery

Q Lv, G Chen, Z Yang, W Zhong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Finding candidate molecules with favorable pharmacological activity, low toxicity, and
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …

Predicting the frequencies of drug side effects

D Galeano, S Li, M Gerstein, A Paccanaro - Nature communications, 2020 - nature.com
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in
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 …

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

DM Bean, H Wu, E Iqbal, O Dzahini, ZM Ibrahim… - Scientific reports, 2017 - nature.com
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 …

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 …

Predicting potential side effects of drugs by recommender methods and ensemble learning

W Zhang, H Zou, L Luo, Q Liu, W Wu, W Xiao - Neurocomputing, 2016 - Elsevier
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 …