Image 1_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”

Image 2_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Elevated NUBPL expression levels can impair the function of chemokines. Moreover, patients with lower NUBPL expression levels exhibit better responses to immunotherapy. …”

Table 3_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Table 2_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 3_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 2_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Image 1_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.tif by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Table 1_Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.xlsx by Weiran Zhang (411189)

“…Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk transcriptomic data, and ten machine learning algorithms were applied to construct the EGFR Mutation-Associated Malignant Epithelial Cell-Related Signature (EGFRmERS). …”

Data Sheet 1_Investigating neural markers of Alzheimer's disease in posttraumatic stress disorder using machine learning algorithms and magnetic resonance imaging.pdf by Gabriella Yakemow (20137758)

“…Additionally, we utilized two previously established machine learning-based algorithms, one representing AD-like brain activity (Machine learning-based AD Designation [MAD]) and the other focused on AD-like brain structural changes (AD-like Brain Structure [ABS]). …”

Table 7 displays the results of evaluations performed on fixed-dimension multimodal benchmark functions. by Ruiyu Zhan (21602031)

Data Sheet 1_AI-enabled workforce integration: blended human resource contribution rate in Chinese companies.pdf by Kexin Zhang (301752)

Parametric study of accuracy of the Random forest algorithm for different proportions of test samples and different choice of random states. by Abeer Aljohani (18497914)

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Supplementary file 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.docx by Jing-Hong Xiao (22780781)

“…After standard quality control, 295,589 SNPs were retained. Fourteen machine-learning algorithms were evaluated using SNPs selected through traditional GWAS filtering and refined via wrapper-based feature selection with a best-first search algorithm. …”

Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

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Data Sheet 2_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip by Yonghua Pang (20998022)

“…To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).…”

Data Sheet 1_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip by Yonghua Pang (20998022)

“…To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).…”

Cognitive test and self-perceived health and social functioning presented for the low-moderate and high PA levels group. by Xin Zhang (35492)

Subjects:

Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches by Lisa Lombardo (7301969)

Subjects:

Quantum Chemistry-Guided Machine Learning for Accelerated Design of CO₂‑Solubilizing Deep Eutectic Solvents by Dingkai Hu (16277547)

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Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”