Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

ABSTRACT: The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. In this model, two different sets of data were used which include enzymes that can catalyze the toxin degradation as a positive dataset and non-toxin-degrading enzymes as a negative dataset. Further, a comparison of multiple classifiers was performed to find the best model and a Random Forest (RF) classifier was selected due to its strong performance. To enhance the accuracy, we combined RF with a Deep Neural Network (DNN), forming an ensemble model which e

SUBMITTER: Sahibzada KI 

PROVIDER: S-EPMC12031443 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature