Enhancing In Vitro Toxicology Through Machine Learning

Applied In Vitro Toxicology is calling for original research, commentary, and review manuscripts that address the application of machine learning (ML) to in vitro toxicology. The particular emphasis is innovation that results in the replacement of animals in toxicology assessment and experiments. A previous Applied In Vitro Toxicology special issue centered on the integration of multiple sources of information; for this issue we are interested specifically in the melding of human data analytics with human cell and tissue systems that advance in vitro toxicology.

Fundamental research in this field is blossoming, with a recent search on PubMed revealing a 5-fold increase in ML AND toxicology publications (28 / 144) between 2016 to 2021. With this level of interest, now is the time to synthesise and translate fundamental research into day-to-day technologies and animal-free innovation.

The Applied In Vitro Toxicology special issue will be interested in the full range of ML algorithms (e.g., support vector machines, recursive partitioning) as applied to problems associated with detecting and measuring toxicity in cell culture systems. While predictive modelling is a powerful application of ML, we will also be interested in other methods that support the characterisation of toxicity pathways, calculation of endpoints, as well as provide insights into pathology post-toxic exposure. Integration of ML with broader statistical analysis frameworks will also be considered in this context.

Submission Deadline: All manuscripts should be submitted for consideration by September 30, 2022.

Visit the Journal's website to learn more, read past issues, and view author submission guidelines. Thank you and we are looking forward to taking into consideration your work for our upcoming special issue.