Image 4_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 8_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Table 1_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 3_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 9_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 6_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 5_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Table 2_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 10_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in pre... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 2_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 7_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Image 1_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec... by Yutong Fang (16621143)

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”

Fitting single-cell data to the stochastic model. by Benjamin D. Maier (13994415)

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Table_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.xlsx by Yao Gao (1420339)

“…Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. …”

Image_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.tif by Yao Gao (1420339)

“…Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…Model evaluation was based on accuracy metrics and qualitative analysis of the confusion matrix.. …”

Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx by Hao Dai (4277254)

Image_2_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.tif by Xin Qing (10730751)

“…The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. …”

Image_1_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.tif by Xin Qing (10730751)

“…The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. …”