Table 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.docx حسب Yuxuan Li (1674307)

Image 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.tif حسب Yuxuan Li (1674307)

Haplotype phylogenies and iSNV frequency trajectories across sampling time points (patient A and J). حسب Lele Zhao (532080)

Variable selection by RFE. حسب Xiaoli Shen (2208601)

Frequency trajectories of iSNVs that fluctuated (Top) and their positions on the genome (Bottom) (patients A-I). حسب Lele Zhao (532080)

Spatial transcriptomics (ST) and single-cell atlases of multiple human tissues. حسب Huamei Li (8815955)

Decision tree of predictive models developed using important variable peaks from decision-tree feature selection (Z-score scaled intensity). حسب Tran Duong Thai (20939598)

Average accuracy by feature selection method. حسب Şafak Kılıç (22227019)

"…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. …"

Reconstructed haplotype trees showed different topologies across individuals. حسب Lele Zhao (532080)

Image 2_Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.tif حسب Shuyu Wen (15411107)

Image 1_Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.tif حسب Shuyu Wen (15411107)

Data Sheet 1_Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study.docx حسب Shuyu Wen (15411107)

Background field-corrected field maps in native space of a representative subject (38 years old female; RMS patient [EDSS = 0]) from additional BFR algorithms (listed on the left-h... حسب Fahad Salman (6150395)

KS statistic plot in predicting LAT/SEC risk. حسب Chaoqun Huang (529718)

"…A retrospective study of 1,222 NVAF patients was conducted. Nine ML algorithms combined with demographic, clinical, and laboratory data were applied to develop prediction models for LAT/SEC in NVAF patients. …"

Multivariate logistic regression analysis. حسب Chaoqun Huang (529718)

"…A retrospective study of 1,222 NVAF patients was conducted. Nine ML algorithms combined with demographic, clinical, and laboratory data were applied to develop prediction models for LAT/SEC in NVAF patients. …"

The study flowchart. حسب Nguyen Tat Thanh (10296398)

"…Multiple supervised machine-learning algorithms – logistic regression, random forest (RF), support vector machine (SVM), k-nearest neighbor, naïve Bayes, AdaBoost, and XGBoost - were applied. …"

Missing value chart of candidate variables. حسب Nguyen Tat Thanh (10296398)

"…Multiple supervised machine-learning algorithms – logistic regression, random forest (RF), support vector machine (SVM), k-nearest neighbor, naïve Bayes, AdaBoost, and XGBoost - were applied. …"

Data Sheet 1_Establishment and evaluation of a model for clinical feature selection and prediction in gout patients with cardiovascular diseases: a retrospective cohort study.zip حسب Bingbing Fan (1438732)

"…A logistic regression model was constructed based on eight variables selected using the Boruta feature selection algorithm: sex, age, PLT, EOS, LYM, CO2, GLU and APO-B. …"

Supplementary file 1_Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models.pdf حسب Wentian Li (28857)

Data Sheet 1_Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.docx حسب Ankita Lawarde (16544943)

"…Using transcriptomic profiles from 14 cancer types in The Cancer Genome Atlas (TCGA), we constructed co-expression networks and applied multiple feature selection techniques including recursive feature elimination (RFE), random forest (RF), Boruta, and linear discriminant analysis (LDA) to identify a minimal yet informative subset of miRNA features. …"