Predicting Dose-Range Chemical Toxicity using Novel Hybrid Deep Machine-Learning Method.

ABSTRACT: Humans are exposed to thousands of chemicals, including environmental chemicals. Unfortunately, little is known about their potential toxicity, as determining the toxicity remains challenging due to the substantial resources required to assess a chemical in vivo. Here, we present a novel hybrid neural network (HNN) deep learning method, called HNN-Tox, to predict chemical toxicity at different doses. To develop a hybrid HNN-Tox method, we combined two neural network frameworks, the Convolutional Neural Network (CNN) and the multilayer perceptron (MLP)-type feed-forward neural network (FFNN). Combining the CNN and FCNN in the field of environmental chemical toxicity prediction is a novel approach. We developed several binary and multiclass classification models to assess dose-range chemical

SUBMITTER: Limbu S 

PROVIDER: S-EPMC9692315 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature