Interpretable machine learning for investigating complex nanomaterial–plant–soil interactions

Yu, Hengjie; Zhao, Zhimin; Luo, Dan; Fang, Cheng

doi:10.1039/d2en00181k

Cited by 4 publications

(13 citation statements)

References 57 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RuleFit algorithm supplied each rule’s coefficient, support, and importance. The importance calculation of an original feature was based on the division of rule importance . To identify important features, the average feature importance was calculated, and the strength of average feature interactions was determined by summing the importance of rules containing related features.…”

Section: Methodsmentioning

confidence: 99%

“…The importance calculation of an original feature was based on the division of rule importance. 22 To identify important features, the average feature importance was calculated, and the strength of average feature interactions was determined by summing the importance of rules containing related features. However, the effects of each feature were distributed across numerous rules, making it difficult to intuitively explain.…”

Section: Materials and Chemicalsmentioning

confidence: 99%

“…Therefore, the combination of post-hoc and model-based interpretation methods could be ideal. In our previous studies, their combination was used to investigate the root uptake of metal-oxide nanoparticles in the soil environment, 22 and two ways of the integration of post-hoc and model-based interpretation methods were proposed to study nanoparticle uptake during seed priming and its biological effects. 23 The data gap is another big challenge for the combination of interpretable machine learning and nano-enabled strategies.…”

Section: ■ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

Yu,

Tang,

et al. 2023

Environ. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

Understanding plant uptake and translocation of nanomaterials is crucial for ensuring the successful and sustainable applications of seed nanotreatment. Here, we collect a dataset with 280 instances from experiments for predicting the relative metal/ metalloid concentration (RMC) in maize seedlings after seed priming by various metal and metalloid oxide nanoparticles. To obtain unbiased predictions and explanations on small datasets, we present an averaging strategy and add a dimension for interpretable machine learning. The findings in post-hoc interpretations of sophisticated LightGBM models demonstrate that solubility is highly correlated with model performance. Surface area, concentration, zeta potential, and hydrodynamic diameter of nanoparticles and seedling part and relative weight of plants are dominant factors affecting RMC, and their effects and interactions are explained. Furthermore, self-interpretable models using the RuleFit algorithm are established to successfully predict RMC only based on six important features identified by post-hoc explanations. We then develop a visualization tool called RuleGrid to depict feature effects and interactions in numerous generated rules. Consistent parameter-RMC relationships are obtained by different methods. This study offers a promising interpretable data-driven approach to expand the knowledge of nanoparticle fate in plants and may profoundly contribute to the safety-by-design of nanomaterials in agricultural and environmental applications.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Materials and Chemicalsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

Yu,

Tang,

et al. 2023

Environ. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Model interpretation workflow, post hoc interpretation, and model-based interpretation were conducted following the procedures described in our previous studies with some adjustments. [28][29][30] Briefly, different LightGBM models were established based on ten random dataset splits, and LightGBM importance, permutation importance, and SHAP importance were employed to identify the important features for ten models. Their average feature importance rank was used to identify the most relevant features for correlating the root dry weight.…”

Section: Model Establishment and Model Interpretationmentioning

confidence: 99%

Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation

Luo

et al. 2023

Nanoscale

View full text Add to dashboard Cite

show abstract

“…Machine learning has been demonstrated to be a tool for aiding in material generation [69][70][71][72][73][74] and nanoparticle synthesis [75][76][77]. These techniques have also been used to determine the biological impact of nanomaterials [78][79][80] which would be important for preventing environmental damage. In addition, these techniques have been turned towards the use of nanomaterials in agriculture, such as the design of a nanoagrochemical delivery system [11,81], predicting the potential impact of nanoagrochemicals on cultivar growth and yield [82] and the use of nanomaterials for seed priming [83].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Arellano Vidal,

Govan

2024

Agronomy

View full text Add to dashboard Cite

Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.

show abstract

Interpretable machine learning for investigating complex nanomaterial–plant–soil interactions

Abstract: Soil serves as the main recipient of engineered nanomaterials (ENMs). The understanding of complex nanomaterial-plant-soil interactions is urgently needed to keep pace with the safety concerns of ENMs. Machine learning,...

Cited by 4 publications

References 57 publications

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation

Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications

Contact Info

Product

Resources

About