We use cookies to enhance your experience. By continuing to browse this site you agree to our use of cookies. More info.
Engineered nanomaterials (ENMs) have found their applications in various technologies and consumer products. Manipulating chemicals at the nanoscale range introduces unique characteristics to these materials and makes them desirable for technological applications.
Study: Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity. Image Credit: Michael Traitov/Shutterstock.com
With the increasing production of ENMs, there have been adverse effects on the environment. Moreover, it is unfeasible to estimate the risks caused by ENMs each time via in vivo or in vitro experiments. To this end, in silico methods can come to the rescue to perform such evaluations.
In an article published in the journal Chemosphere, the performance of different machine learning algorithms was investigated for predicting well-defined in vivo toxicity endpoint and to identify the important features involved with in vivo nanotoxicity of Daphnia magna.
The results revealed comparable performances of all algorithms and the predictive performance exceeded approximately 0.7 for all metrices evaluated. Furthermore, artificial neural network, random forest, and k-nearest neighbor models showed a marginally better performance compared to the other algorithm models.
The variable importance analysis performed to understand the significance of input variables revealed that physicochemical properties and molecular descriptors were important within most models. On the other hand, properties related to exposure conditions gave conflicting results. Thus, the machine learning models helped generate in vivo endpoints, even with smaller datasets, demonstrating their reliability and robustness.
Nanotechnology has emerged as a key technology with implications agriculture, medicine, and food industries. Thus, ENMs are more appealing than their larger counterparts due to their outstanding features owing to their smaller size.
Despite their advantages, ENMs have also caused effects on the environment, impacting the health and safety of the environment, calling for environmental risk assessment associated with ENMs. However, this assessment via in vivo or in vitro testing for all fabricated nanoforms is impractical.
The challenge in risk assessment is not only due to extensive ENM production and applications but also due to the large diversity of materials. To this end, chemical modification at the nanoscale range may modulate the physicochemical properties and consequential toxicity profile of the materials.
Recent advances in machine learning offered new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanotechnology use machine learning tools to tackle challenges in many fields. Due to their compatibility with complex interactions, machine learning can help predict the toxicological effects of ENMs through large data sets.
The field of nanotoxicology lacks standardized procedures to depict common ontologies to measure ENM properties. However, the models from limited datasets can help generate the key nanotoxicological descriptors. The nanotoxicological models based on machine learning developed to date focused on endpoints like viability or cytotoxicity.
Despite considerable efforts, various obstacles still exist for in silico modeling of nanotoxicological effects due to limited data availability and poor data curation. Hence, better agreement on data quality, experimental protocols, and availability are vital to acquiring homogenous data across different studies.
In the present work, the performance of machine learning algorithms for predicting in vivo nanotoxicity of metallic ENMs towards Daphnia magna was investigated. Various models were generated based on the sources obtained from immobilization data, which were in congruence with the principles of organization for economic co-operation and development (OECD). Furthermore, the limitations in obtaining consistent data for modeling were overcome by applying different methods of data curation.
Among the six machine learning models generated based on OECD, neural network, random forest, and k-nearest neighbor algorithms showed the highest performance, while the other models showed relatively similar performance. This indicates that machine learning is more suitable for in silico modeling of in vivo nanotoxicity than the actual algorithm. Additionally, key descriptors that modulated the toxicity of metallic ENMs towards Daphnia magna were also studied based on the generated machine learning models.
To summarize, machine learning algorithms were performed to predict the in vivo nanotoxicity of metallic ENMs. The collected Daphnia magna toxicity data for metallic ENMs were analyzed using six classification machine learning models based on the principles of OECD.
The results revealed that artificial neural networks, random forest, and k-nearest neighbor algorithms had the highest performances, which were in line with previous reports from the literature. On the other hand, the relative differences in other algorithm models were comparatively small. These results proved the compatibility of machine learning for in silico modeling of in vivo nanotoxicity.
Furthermore, feature importance analysis using machine learning algorithms revealed contradictory results in all the models, with physicochemical properties and molecular descriptors being significant features within models. The results demonstrated that the models with small datasets with few physicochemical properties and molecular descriptors result in machine learning models with good predictive performance.
Balraadjsing,S., Peijnenburg, W J.G.M., Vijver, M.G (2022) Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity. Chemosphere. https://www.sciencedirect.com/science/article/pii/S0045653522024237
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.
Written by
Bhavna Kaveti is a science writer based in Hyderabad, India. She has a Masters in Pharmaceutical Chemistry from Vellore Institute of Technology, India, and a Ph.D. in Organic and Medicinal Chemistry from Universidad de Guanajuato, Mexico. Her research work involved designing and synthesizing heterocycle-based bioactive molecules, where she had exposure to both multistep and multicomponent synthesis. During her doctoral studies, she worked on synthesizing various linked and fused heterocycle-based peptidomimetic molecules that are anticipated to have a bioactive potential for further functionalization. While working on her thesis and research papers, she explored her passion for scientific writing and communications.
Please use one of the following formats to cite this article in your essay, paper or report:
APA
Kaveti, Bhavna. (2022, August 15). Researchers Assess How Well Machine Learning Predicts Nanotoxicology. AZoNano. Retrieved on August 16, 2022 from https://www.azonano.com/news.aspx?newsID=39554.
MLA
Kaveti, Bhavna. "Researchers Assess How Well Machine Learning Predicts Nanotoxicology". AZoNano. 16 August 2022. <https://www.azonano.com/news.aspx?newsID=39554>.
Chicago
Kaveti, Bhavna. "Researchers Assess How Well Machine Learning Predicts Nanotoxicology". AZoNano. https://www.azonano.com/news.aspx?newsID=39554. (accessed August 16, 2022).
Harvard
Kaveti, Bhavna. 2022. Researchers Assess How Well Machine Learning Predicts Nanotoxicology. AZoNano, viewed 16 August 2022, https://www.azonano.com/news.aspx?newsID=39554.
Do you have a review, update or anything you would like to add to this news story?
Cancel reply to comment
Ping Wang, Ph.D.
We speak with researchers behind the latest advancement in graphene hBN research that could boost the development of next-generation electronic and quantum devices.
Dr. Laurene Tetard
AZoNano speaks with Dr. Laurene Tetard from the University of Central Florida about her upcoming research into the development of nanotechnology that can detect animal-borne diseases. The hope is that such technology can be used to help rapidly control infected mosquito populations to protect public
Dr. Amir Sheikhi
AZoNano speaks with Dr. Amir Sheikhi from Pennsylvania State University about his research into creating a new group of nanomaterials designed to capture chemotherapy drugs before they impact healthy tissue, amending a fault traditionally associated with conventional nanoparticles.
The Filmetrics R54 advanced sheet resistance mapping tool for semiconductor and compound semiconductor wafers.
This product profile describes the latest nano-particle analyzer "thesis particle size analyzer" and its key features.
The Filmetrics F40 turns your benchtop microscope into an instrument for measuring thickness and refractive index.
AZoNano.com – An AZoNetwork Site
Owned and operated by AZoNetwork, © 2000-2022