Data Sheet 1_Stemness-related gene signatures as a predictive tool for breast cancer radiosensitivity.pdf حسب Jinzhi Lai (6856568)

"…Based on these genes, a radiosensitivity signature comprising two stemness-related genes (EMILIN1 and CYP4Z1) was constructed, stratifying patients into radiosensitive (RS) and radioresistant (RR) groups. …"

Table_3_Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.XLSX حسب Jing Li (10611)

"…Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. …"

Table_1_Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.XLSX حسب Jing Li (10611)

"…Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. …"

Table_4_Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.XLSX حسب Jing Li (10611)

"…Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. …"

Table_2_Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.XLSX حسب Jing Li (10611)

"…Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. …"

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

Table_1_Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.xlsx حسب Joanna Tobiasz (11816504)

"…</p>Results<p>SF2 ranged between 0.1384 and 0.4860 (mean=0.3273 The DP algorithm provided optimal segmentation by defining batches of radio-sensitive (RS), normally-sensitive (NS), and radio-resistant (RR) responders. …"