Prediction of the fluoride contents of different crop species via the random forest algorithm

Zhang, Yuqi; Luo, Jie; Feng, Siyao; Ke, Xinying; Jia, Heran; Zhu, Qiaohui

doi:10.1007/s10653-024-02206-w

Environ Geochem Health

2024

DOI: 10.1007/s10653-024-02206-w

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Prediction of the fluoride contents of different crop species via the random forest algorithm

Yuqi Zhang,

Jie Luo,

Siyao Feng

et al.

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Cited by 3 publications

References 37 publications

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Fluoride contamination a silent global water crisis: A Case of Africa

Hossein,

Rwiza,

Nyanza

et al. 2024

Scientific African

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Fluoride contamination a silent global water crisis: A Case of Africa

Hossein,

Rwiza,

Nyanza

et al. 2024

Scientific African

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Research on Sales Dynamic Forecasting Method Based on Time Series Analysis in Global Supply Chain Environment

Zhu

2024

Applied Mathematics and Nonlinear Sciences

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This paper uses time series analysis to forecast sales dynamics in a global supply chain environment. The study selects supermarket chain data with typical time series characteristics and extracts features from it. We combine the delay operator’s difference operation with the ARMA model to construct the ARIMA model, which predicts and analyzes the sales volume of the supermarket chain dataset from 2016 to 2018. To address the issue of time series models being susceptible to nonlinear characteristics and random variables, this paper integrates the ARIMA model and the random forest RF model into a simple weighted average, forming the ARIMA-RF model. The accuracy of the model for sales volume prediction has greatly improved compared to the ARIMA model and RF model, with an accuracy rate as high as 99%. Meanwhile, the ARIMA-RF model has the smallest RMSE value among all the compared models and has the highest prediction accuracy among all the models for the sales volume in the 3rd quarter of 2018.

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Elucidating macrophage scavenger receptor 1’s mechanistic contribution as a shared molecular mediator in obesity and thyroid cancer pathogenesis via bioinformatics analysis

Shang,

Xu,

Wang

et al. 2024

Front. Genet.

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IntroductionObesity is a disease characterized by the excessive accumulation of fat. Concurrently, thyroid carcinoma (THCA) stands as the foremost endocrine malignancy. Despite the observed escalation in concurrent prevalence of both conditions, the underlying interconnections remain elusive. This indicates the need to identify potential biomarkers to predict the pathways through which obesity and THCA coexist.MethodsThe study employed a variety of methods, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and gene enrichment analysis. It was also supplemented with immunohistochemical data from the Human Protein Atlas (HPA), advanced machine learning techniques, and related experiments such as qPCR, to identify important pathways and key genes shared between obesity and THCA.ResultsThrough differential gene expression analysis, WGCNA, and machine learning methods, we identified three biomarkers (IL6R, GZMB, and MSR1) associated with obesity. After validation analysis using THCA-related datasets and biological experiments, we selected Macrophage Scavenger Receptor 1 (MSR1) as a key gene for THCA analysis. The final analysis revealed that MSR1 is closely related to the degree of immune cell infiltration in patients with obesity and THCA, suggesting that this gene may be a potential intervention target for both obesity and THCA.DiscussionOur research indicates that MSR1 may influence the occurrence and development of obesity and THCA by regulating the infiltration level of immune cells. This lays the foundation for future research on targeted therapies based on their shared mechanisms.

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Prediction of the fluoride contents of different crop species via the random forest algorithm

Cited by 3 publications

References 37 publications

Fluoride contamination a silent global water crisis: A Case of Africa

Fluoride contamination a silent global water crisis: A Case of Africa

Research on Sales Dynamic Forecasting Method Based on Time Series Analysis in Global Supply Chain Environment

Elucidating macrophage scavenger receptor 1’s mechanistic contribution as a shared molecular mediator in obesity and thyroid cancer pathogenesis via bioinformatics analysis

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