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

IF 3.1 Q2 TOXICOLOGY
B. Latha , Sheena Christabel Pravin , J. Saranya , E. Manikandan
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引用次数: 2

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.

基于集成超级学习器的多壁碳纳米管遗传毒性预测
多个单壁碳纳米管串联成同心圆柱体,构成多壁碳纳米管。由于其独特的物理和化学特性,多壁碳纳米管在各个领域都有广泛的应用。文献调查研究揭示了多壁碳纳米管的毒性。因此,了解和预测它们的遗传毒性对公共安全具有重要意义。基于深度学习的碳纳米管毒性谱预测,将加速多壁碳纳米管产品毒性减轻研究。提出的混合深度学习框架预测多壁碳纳米管变体的遗传毒性具有更高的准确性和精度。所提出的集成超级学习者(ESL)是一个混合模型,由三个机器学习模型和深度自动编码器级联而成。当对多壁碳纳米管变体的遗传毒性谱进行稀疏数据训练时,该模型达到了百分之几的准确率。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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