Ensemble super learner based genotoxicity prediction of multi-walled carbon nanotubes

Abstract

Multiple single-walled carbon nanotubes, nestled in tandem as concentric cylinders, constitute the multi-walled carbon nanotubes. Due to their unique physical and chemical characteristics, the multi-walled carbon nanotubes find applications over diverse fields. Investigational studies in the literature reveal toxic nature of multi-walled carbon nanotubes. Hence, it is important to sense and predict their genotoxicity profile for public safety. Deep learning-based toxicity profile prediction, would hasten the research in the alleviation of toxicity in the products build using the multi-walled carbon nanotubes. The proposed hybrid-deep learning framework predicts the genotoxicity of variants of multi-walled carbon nanotubes with higher accuracy and precision. The proposed Ensemble Super Learner (ESL) is a hybrid model, built as a cascade combination of three machine learning models and deep autoencoder. The model achieves cent-percent accuracy when trained over the sparse data available on the genotoxic profile of variants of multi-walled carbon nanotubes.