围绕化学品致癌潜力的担忧不断升级,强调了对评估致癌性的有效方法的迫切需要。传统的实验方法,如体外和体内测定,尽管有效,遭受昂贵和耗时的痛苦。为了应对这一挑战,新的替代方法,特别是机器学习和深度学习技术,因其在开发致癌性预测模型方面的潜力而受到关注。本文综述了使用各种机器学习和深度学习算法预测致癌性的进展。对这些开发模型的比较分析表明,支持向量机,随机森林,和集成学习在预测化学致癌性方面的鲁棒性和有效性通常是首选。相反,基于深度学习算法的模型,比如前馈神经网络,卷积神经网络,图卷积神经网络,胶囊神经网络,和混合神经网络,表现出有希望的能力,但受到可用致癌性数据集大小的限制。这篇综述全面分析了当前用于致癌性预测的机器学习和深度学习模型,强调高质量和大型数据集的重要性。这些观察预计将促进未来在开发有效和可推广的机器学习和深度学习模型以预测化学致癌性方面的进步。
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing
carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing
carcinogenicity prediction models. This article reviews the progress in predicting
carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical
carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available
carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.