Table 3_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Table 10_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Image 11_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Image 4_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Image 7_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Table 9_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Table 5_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Table 8_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Table 7_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Image 1_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Image 13_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Table 11_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Image 12_Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherap... by Weifeng Wang (83822)

“…</p>Results<p>We developed a prognostic risk model comprising 13 genes through the application of multiple machine learning algorithm sand and confirmed as an independent prognostic factor. …”

Comparison of Five Classification Models by Qiran Wang (22051883)

“…The K-Nearest Neighbors (KNN) algorithm assigns class labels based on the majority vote of the nearest neighbors in the feature space, making it simple yet effective for small-scale datasets. …”

Data Sheet 1_Use of machine learning models to predict mortality in dialysis patients.pdf by Junmin Huang (2431492)

“…Each model underwent hyperparameter optimization based on three performance metrics (accuracy, F1-score, and ROC Area Under the Curve [AUC]) to evaluate the impact of different clinical priorities.…”

Additional data for the polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…</p><p dir="ltr">All NEB calculations are performed using the ASE version 3.23.0 NEB wrapper with the FIRE optimization algorithm. Five intermediate images were used in the NEB optimization, converging to a maximum atom-wise force of 0.03ev/Å. …”

Supplementary file 2_QSAR-guided discovery of novel KRAS inhibitors for lung cancer therapy.docx by Osasan Stephen Adebayo (22630082)

“…Following descriptor normalization and dimensionality reduction, five machine learning algorithm spartial least squares (PLS), random forest (RF), stepwise multiple linear regression (MLR), genetic algorithm optimized MLR (GA-MLR), and XGBoost were applied. …”

Table 4_QSAR-guided discovery of novel KRAS inhibitors for lung cancer therapy.xlsx by Osasan Stephen Adebayo (22630082)

“…Following descriptor normalization and dimensionality reduction, five machine learning algorithm spartial least squares (PLS), random forest (RF), stepwise multiple linear regression (MLR), genetic algorithm optimized MLR (GA-MLR), and XGBoost were applied. …”

Table 1_QSAR-guided discovery of novel KRAS inhibitors for lung cancer therapy.xlsx by Osasan Stephen Adebayo (22630082)

“…Following descriptor normalization and dimensionality reduction, five machine learning algorithm spartial least squares (PLS), random forest (RF), stepwise multiple linear regression (MLR), genetic algorithm optimized MLR (GA-MLR), and XGBoost were applied. …”

Table 3_QSAR-guided discovery of novel KRAS inhibitors for lung cancer therapy.xlsx by Osasan Stephen Adebayo (22630082)

“…Following descriptor normalization and dimensionality reduction, five machine learning algorithm spartial least squares (PLS), random forest (RF), stepwise multiple linear regression (MLR), genetic algorithm optimized MLR (GA-MLR), and XGBoost were applied. …”