Data Sheet 1_A novel lactylation-related gene signature to predict prognosis and treatment response in lung adenocarcinoma.docx by Hongyi Zhang (431841)

“…Additionally, various algorithms were used to explore the relationship between the risk score and immune infiltration levels, with model genes analyzed based on single-cell sequencing. …”

Table_3_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Image_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Image_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.JPEG by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Table_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.XLSX by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Data_Sheet_2_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.docx by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Data_Sheet_1_Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression.CSV by Qin Jiang (503001)

“…In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. …”

Table2_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.XLSX by Yaxian Song (10454804)

“…The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.…”

Table1_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.XLSX by Yaxian Song (10454804)

“…The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.…”

Presentation1_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.PDF by Yaxian Song (10454804)

“…The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.…”

Image1_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.PDF by Yaxian Song (10454804)

“…The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.…”

DataSheet_1_Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy.docx by Qianqian Yan (4479328)

“…The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. …”

Table3_Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.XLSX by ZhanDong Li (11653330)

Table4_Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.XLSX by ZhanDong Li (11653330)

Table1_Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.XLSX by ZhanDong Li (11653330)

Table2_Identifying In Vitro Cultured Human Hepatocytes Markers with Machine Learning Methods Based on Single-Cell RNA-Seq Data.XLSX by ZhanDong Li (11653330)

Data Sheet 1_Immunogenic cell death-related genes as prognostic biomarkers and therapeutic insights in uterine corpus endometrial carcinoma: an integrative bioinformatics analysis.... by Tianfei Yi (10971822)

“…</p>Methods<p>The ICD score was assessed using single-sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) were identified from transcriptomic data processed with the "DESeq2" R package. …”

Data_Sheet_1_Metagenomic Geolocation Prediction Using an Adaptive Ensemble Classifier.PDF by Samuel Anyaso-Samuel (10671576)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…<p dir="ltr">The first algorithm for segmentation and localization (see PathOlOgics_script_1; segment & localize using a pen) relied on manually tracing the borders of each cell using a digital pen tool on a big touchscreen display showing source images/patches. …”

Table_3_NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.xlsx by Yuchen Zhang (524816)

“…An information theory-based score, weighted information gain (WIG), was proposed to assess the impact of signaling genes on a specific downstream biological process of interest. …”