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

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

Multiple machine learning methods for screening heart failure related diagnostic biomarkers. by Ming Du (532508)

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

Subjects:

Ocean plot of windows with strong evidence for the maintenance of population structure after treatment. by Lele Zhao (532080)

Subjects:

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

Subjects:

A. Distributions of the observed (red), 1000 Poisson (green) and 1000 negative binomial (blue) model generated expected counts of synonymous mutations per genome per day. B. Estima... by Lele Zhao (532080)

Subjects:

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

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Variable selection by RFE. by Xiaoli Shen (2208601)

Table 6_Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.xlsx by Jingyun Jin (10968678)

“…Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), with diagnostic accuracy assessed via Receiver Operating Characteristic (ROC) curves and nomograms to screen hub genes, and external datasets further were used for validation. …”

Table 5_Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.xlsx by Jingyun Jin (10968678)

“…Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), with diagnostic accuracy assessed via Receiver Operating Characteristic (ROC) curves and nomograms to screen hub genes, and external datasets further were used for validation. …”

Table 2_Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.xlsx by Jingyun Jin (10968678)

“…Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), with diagnostic accuracy assessed via Receiver Operating Characteristic (ROC) curves and nomograms to screen hub genes, and external datasets further were used for validation. …”

Table 1_Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.xlsx by Jingyun Jin (10968678)

“…Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), with diagnostic accuracy assessed via Receiver Operating Characteristic (ROC) curves and nomograms to screen hub genes, and external datasets further were used for validation. …”

Table 3_Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.xlsx by Jingyun Jin (10968678)

“…Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), with diagnostic accuracy assessed via Receiver Operating Characteristic (ROC) curves and nomograms to screen hub genes, and external datasets further were used for validation. …”

Table 4_Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.xlsx by Jingyun Jin (10968678)

“…Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), with diagnostic accuracy assessed via Receiver Operating Characteristic (ROC) curves and nomograms to screen hub genes, and external datasets further were used for validation. …”

The study flowchart. by 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. by 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. …”

Table 1_Rapid forensic ancestry inference in selected Northeast Asian populations: a Y-STR based attention-based ensemble framework for initial investigation guidance.xlsx by Kyo-Chan Koo (22263367)