Dr. Adrian Green (Trainee) was awarded the 2022 Elsevier Postdoctoral Award by the Computational Toxicology Specialty Section of the Society of Toxicology for his abstract entitled “Pattern recognition in high-dimensional zebrafish behavioral studies using autoencoder based deep learning”.
Adrian will present in the SOT 2022 platform session “Deep Learning and Graph Algorithms: New Approaches in Computational Toxicology”. Adrian conceived this session and led its development with colleagues from the U.S. EPA and the company Neo4j. It builds from his recent PLoS Computational Biology paper entitled “Leveraging highthroughput screening data and conditional generative adversarial networks to advance predictive toxicology”. He led the team of toxicologists, chemists, mathematicians, and modelers to conceive, design, execute, and publish this study. This predictive toxicology paper used a Deep Neural Network (DNN) and a conditional Generative Adversarial Network (cGAN) to analyze an enormous set of experimental data on chemical toxicity in zebrafish. The resulting predictive model could have far-reaching impacts in illustrating how to handle the mass of untested (and under-tested) compounds in the exposome.