Rodrigues Nuno M, Batista João E, Mariano Pedro, Fonseca Vanessa, Duarte Bernardo, Silva Sara
LASIGE, Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal.
Biosystems and Integrative Sciences Institute (BioISI), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal.
Biology (Basel). 2021 Sep 18;10(9):932. doi: 10.3390/biology10090932.
Over recent decades, the world has experienced the adverse consequences of uncontrolled development of multiple human activities. In recent years, the total production of chemicals has been composed of environmentally harmful compounds, the majority of which have significant environmental impacts. These emerging contaminants (ECs) include a wide range of man-made chemicals (such as pesticides, cosmetics, personal and household care products, pharmaceuticals), which are of worldwide use. Among these, several ECs raised concerns regarding their ecotoxicological effects and how to assess them efficiently. This is of particular interest if marine diatoms are considered as potential target species, due to their widespread distribution, being the most abundant phytoplankton group in the oceans, and also being responsible for key ecological roles. Bio-optical ecotoxicity methods appear as reliable, fast, and high-throughput screening (HTS) techniques, providing large datasets with biological relevance on the mode of action of these ECs in phototrophic organisms, such as diatoms. However, from the large datasets produced, only a small amount of data are normally extracted for physiological evaluation, leaving out a large amount of information on the ECs exposure. In the present paper, we use all the available information and evaluate the application of several machine learning and deep learning algorithms to predict the exposure of model organisms to different ECs under different doses, using a model marine diatom () as a test organism. The results show that 2D convolutional neural networks are the best method to predict the type of EC to which the cultures were exposed, achieving a median accuracy of 97.65%, while Rocket is the best at predicting which concentration the cultures were subjected to, achieving a median accuracy of 100%.
在最近几十年里，世界经历了多种人类活动无节制发展带来的不利后果。近年来，化学品的总产量中包含对环境有害的化合物，其中大多数对环境有重大影响。这些新兴污染物（ECs）包括多种人造化学品（如农药、化妆品、个人和家庭护理产品、药品），它们在全球范围内都有使用。其中，有几种新兴污染物引发了人们对其生态毒理学效应以及如何有效评估这些效应的关注。如果将海洋硅藻视为潜在的目标物种，这一点就尤为重要，因为它们分布广泛，是海洋中最丰富的浮游植物群体，并且还发挥着关键的生态作用。生物光学生态毒性方法似乎是可靠、快速且高通量筛选（HTS）的技术，能提供大量与生物学相关的数据集，涉及这些新兴污染物在光合生物（如硅藻）中的作用模式。然而，从生成的大量数据集中，通常仅提取少量数据用于生理评估，从而遗漏了大量关于新兴污染物暴露的信息。在本文中，我们利用所有可用信息，评估了几种机器学习和深度学习算法的应用，以预测模式生物在不同剂量下对不同新兴污染物的暴露情况，使用一种模式海洋硅藻（）作为测试生物。结果表明，二维卷积神经网络是预测培养物所暴露的新兴污染物类型的最佳方法，中位数准确率达到97.65%，而Rocket在预测培养物所接触的浓度方面表现最佳，中位数准确率达到100%。
