Drug–target interactions (DTIs) play a crucial role in target-based drug discovery and
development. Computational prediction of DTIs can effectively complement experimental …

Explainable machine learning for molecular toxicity prediction is a promising approach for
efficient drug development and chemical safety. A predictive ML model of toxicity can reduce …

Abstract Background Drug-target interactions (DTIs) prediction becomes more and more
important for accelerating drug research and drug repositioning. Drug-target interaction …

Characterizing cyber harms from digital health | Nature Medicine Skip to main content Thank
you for visiting nature.com. You are using a browser version with limited support for CSS. To …

Aim: To characterize perceived benefits and challenges experienced by medicinal cannabis
users. Methods: An anonymous online survey collected demographics, health information …

Background Although there is an enormous number of textual resources in the biomedical
domain, currently, manually curated resources cover only a small part of the existing …

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical
industry. The existing side effect prediction methods mainly focus on the chemical and …

Multiple randomized controlled trials (RCTs) have examined first-line pharmacological
agents such as anticholinergics and β3 agonists for the management of overactive bladder …

Phenotype-based chrysanthemum petal classification, integrated with genomic sequencing,
is critical for studying chrysanthemum phenotypic taxonomy. In this article, a new pipeline …

OPEN ACCESS EDITED BY Page 259 1016/j. cj. 2020.11. 011 00474 frontiersin. org Frontiers
in Genetics 15 257258 Page 260 TYPE Original Research frontiers Frontiers in Genetics …