Predicting nanotoxicity by an integrated machine learning and metabolomics approach

Peng, Ting; Wei, Changhong; Yu, Fubo; Xu, Jing; Zhou, Qixing; Shi, Tonglei; Hu, Xiangang

doi:10.1016/j.envpol.2020.115434

Cited by 34 publications

(17 citation statements)

References 44 publications

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“…A nanotoxicity-based ML classification model was constructed for different nanoparticles using literature-based data focused on cell type, cell origin, assay method, NP type, physicochemical properties, and exposure parameters (route, duration, and concentration) [ 90 ]. Peng et al generated a metabolic pathway-based (amino acid, lipid, carbohydrate, energy, nucleotide, and biosynthesis) prediction model using a nanotoxicity database for 33 kinds of NPs in animals, cells, and plants [ 29 ]. Oh et al studied the correlation between physicochemical properties and toxicity quantum dots using an ML regression model with database from 307 publications further identifying that the shell, ligand, and surface modifications, diameter, assay type, and exposure time were closely correlated with toxicity [ 92 ].…”

Section: ML For the Integration Of Omicsmentioning

confidence: 99%

“…Therefore, analysis with systems toxicology, including integrated omics approaches, will be helpful for the precise assessment of nanotoxicity. Given that big data are used for integrated multi-omics, advanced analysis of discrimination methods such as machine learning (ML) has been introduced for their classification [ 29 ]. For the precise assessment of nanotoxicity, accurate multi-omics data (big data) and the selection of a proper ML algorithm are crucial.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

et al. 2021

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Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

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Section: ML For the Integration Of Omicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

et al. 2021

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“…With the increasing utilization of NMs, the understanding of the risks and toxicity of NMs to biology and the environment requires continuous research and monitoring . ML techniques have been applied in the field of NM biological toxicity with encouraging results. , The RF technique was used to develop a tissue-specific classification model for neurotoxicity prediction caused by nanoparticles in in vitro systems . Data sets consisting of nanoparticle physicochemical properties, exposure conditions, and in vitro characteristics were extracted from 36 articles.…”

Section: Investigation Of the Interactions Between Nms And Biologymentioning

confidence: 99%

Machine Learning Boosts the Design and Discovery of Nanomaterials

Jia

Hou

Wang

et al. 2021

ACS Sustainable Chem. Eng.

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Nanomaterials (NMs) have developed quickly and cover various fields, but research on nanotechnology and NMs largely relies on costly experiments or complex calculations (e.g., density functional theory). In contrast, machine learning (ML) methods can address the large amount of time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of NMs. ML is a powerful tool for NM research; however, large knowledge gaps and critical issues should be promptly addressed to promote NMs from the laboratory to industry. With a focus on the primary NM aspects, enhancements to the design of NM structures, properties, adsorption, and catalysis by ML are reviewed and discussed. Given the emergent challenges in nanobiology, ML predictions of interactions between NMs and biology are also analyzed. Subsequently, this perspective discusses how to improve the interpretability of ML algorithms, which has been a bottleneck of ML in recent years. ML has led to innovations in the development of NMs, but some problems remain, such as imperfect databases and the accuracy of algorithm determination and nanopattern image recognition, which are herein addressed. Overall, this perspective provides insights for the development of ML in NM research.

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“…Data-driven strategies have been making important advances in modeling biological phenomena that have potential usage to evaluate nano-immune interactions, such as predicting biomolecular corona compositions (177)(178)(179)(180)(181), and nanomaterials and cell interactions (e.g., cell uptake, cytotoxicity, membrane integrity, oxidative stress) (182)(183)(184)(185). Furthermore, the exploration of omics approaches (e.g., genomics, transcriptomics, and metabolomics) has promoting the development of ML models to process the complex data generated by these techniques and enables a better understanding of the molecular mechanisms of nanomaterials adverse effects in a systemic context, defining and predicting adverse outcome pathways (186)(187)(188)(189). The omics' potential of data generation is demonstrated by Kinaret et al (190), who were able to connect immune responses to observed transcriptomic alterations in mouse airway exposed to 28 engineered nanomaterials.…”

Section: Nanoinformatics Approaches Toward Immunosafety-by-designmentioning

confidence: 99%

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

et al. 2021

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Two-dimensional (2D) materials have emerged as an important class of nanomaterials for technological innovation due to their remarkable physicochemical properties, including sheet-like morphology and minimal thickness, high surface area, tuneable chemical composition, and surface functionalization. These materials are being proposed for new applications in energy, health, and the environment; these are all strategic society sectors toward sustainable development. Specifically, 2D materials for nano-imaging have shown exciting opportunities in in vitro and in vivo models, providing novel molecular imaging techniques such as computed tomography, magnetic resonance imaging, fluorescence and luminescence optical imaging and others. Therefore, given the growing interest in 2D materials, it is mandatory to evaluate their impact on the immune system in a broader sense, because it is responsible for detecting and eliminating foreign agents in living organisms. This mini-review presents an overview on the frontier of research involving 2D materials applications, nano-imaging and their immunosafety aspects. Finally, we highlight the importance of nanoinformatics approaches and computational modeling for a deeper understanding of the links between nanomaterial physicochemical properties and biological responses (immunotoxicity/biocompatibility) towards enabling immunosafety-by-design 2D materials.

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Predicting nanotoxicity by an integrated machine learning and metabolomics approach

Cited by 34 publications

References 44 publications

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Machine Learning Boosts the Design and Discovery of Nanomaterials

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

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