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process classification » protein classification (Expand Search), proposed classification (Expand Search), forest classification (Expand Search)
based optimization » whale optimization (Expand Search)
library based » laboratory based (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary b » binary _ (Expand Search)
b based » _ based (Expand Search), 1 based (Expand Search), 2 based (Expand Search)
process classification » protein classification (Expand Search), proposed classification (Expand Search), forest classification (Expand Search)
based optimization » whale optimization (Expand Search)
library based » laboratory based (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary b » binary _ (Expand Search)
b based » _ based (Expand Search), 1 based (Expand Search), 2 based (Expand Search)
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61
Supplementary Material 8
Published 2025“…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”
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62
Flow diagram of the automatic animal detection and background reconstruction.
Published 2020“…(E) The threshold value is calculated based on the histogram: it is the mean of the image subtracted by 4 (optimal value defined by trial and error). …”
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63
Table_6_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.XLS
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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64
Table_4_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.XLS
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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65
Table_3_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.XLSX
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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66
Image_1_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.TIF
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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67
Table_1_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.XLS
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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68
Table_5_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.XLS
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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69
Image_2_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.TIF
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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70
Table_2_Identification of Key Genes With Differential Correlations in Lung Adenocarcinoma.XLS
Published 2021“…</p>Conclusion<p>Our study provided new insights into the gene regulatory mechanisms during transition from normal to tumor, pioneering a network-based algorithm in the application of tumor etiology.…”
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71
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”
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72
Data_Sheet_2_Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.docx
Published 2021“…The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.…”
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73
Data_Sheet_1_Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.xlsx
Published 2021“…The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.…”
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74
Data_Sheet_2_Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.docx
Published 2021“…The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.…”
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75
Data_Sheet_1_Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.xlsx
Published 2021“…The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.…”