Fundamentals of protein interaction network mapping

J Snider, M Kotlyar, P Saraon, Z Yao… - Molecular systems …, 2015 - embopress.org
Studying protein interaction networks of all proteins in an organism (“interactomes”) remains
one of the major challenges in modern biomedicine. Such information is crucial to …

Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

J Tang, T Aittokallio - Current pharmaceutical design, 2014 - ingentaconnect.com
Polypharmacology has emerged as novel means in drug discovery for improving treatment
response in clinical use. However, to really capitalize on the polypharmacological effects of …

A similarity-based method for prediction of drug side effects with heterogeneous information

X Zhao, L Chen, J Lu - Mathematical biosciences, 2018 - Elsevier
Drugs can produce intended therapeutic effects to treat different diseases. However, they
may also cause side effects at the same time. For an approved drug, it is best to detect all …

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 …

[HTML][HTML] Detecting potential adverse drug reactions using a deep neural network model

CS Wang, PJ Lin, CL Cheng, SH Tai… - Journal of medical …, 2019 - jmir.org
Background Adverse drug reactions (ADRs) are common and are the underlying cause of
over a million serious injuries and deaths each year. The most familiar method to detect …

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 …

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

P Das, DH Mazumder - Artificial Intelligence Review, 2023 - Springer
Approved drugs for sale must be effective and safe, implying that the drug's advantages
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …

Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models

E Muñoz, V Nováček… - Briefings in …, 2019 - academic.oup.com
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of
public health and pharmacology. Early discovery of potential ADRs can limit their effect on …

Drug side effect prediction through linear neighborhoods and multiple data source integration

W Zhang, Y Chen, S Tu, F Liu… - 2016 IEEE international …, 2016 - ieeexplore.ieee.org
Predicting drug side effects is a critical task in the drug discovery, which attracts great
attentions in both academy and industry. Although lots of machine learning methods have …

A unified frame of predicting side effects of drugs by using linear neighborhood similarity

W Zhang, X Yue, F Liu, Y Chen, S Tu, X Zhang - BMC systems biology, 2017 - Springer
Background Drug side effects are one of main concerns in the drug discovery, which gains
wide attentions. Investigating drug side effects is of great importance, and the computational …