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+This README.md file was generated on 2021-08-23 by Péter Gargya
+
+GENERAL INFORMATION
+
+1. Title of Dataset: Dataset used in the article:  
+    "Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted With Machine Learning From Gene Expression Data" 
+
+2. Author Information  
+A. Principal Investigator Contact Information  
+Name: dr. Bálint László Bálint  
+Institution: Genomic Medicine and Bioinformatics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen  
+Address: Egyetem tér 1, 4032 Debrecen, Hungary  
+Email: lbalint@med.unideb.hu  
+B. Associate or Co-investigator Contact Information  
+Name: Péter Gargya  
+Institution: Genomic Medicine and Bioinformatics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine,   University of Debrecen  
+Address: Egyetem tér 1, 4032 Debrecen, Hungary  
+Email: gargya.peter@gmail.com  
+
+
+SHARING/ACCESS INFORMATION  
+
+1. Licenses/restrictions placed on the data: The data and codes provided are free to use, however we kindly ask everybody to cite our article as written below.  
+
+2. Links to publications that cite or use the data:   
+https://www.mdpi.com/2072-6694/13/17/4348/htm  
+
+3. Recommended citation for this dataset:   
+Gargya, P.; Bálint, B.L. Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data. Cancers 2021, 13, 4348. https://doi.org/10.3390/cancers13174348 
+
+DATA & FILE OVERVIEW  
+
+1. File List:  
+- part1_R_prepare_data.Rmd: Codes used to produce the data before applying machine learning.
+- part2_Python_ML.py: Creating our Machine Learning model
+- part3_R_survival_analysis.Rmd: Survival analysis between low-risk and high-risk G2 subgroups, which were defined by our model.
+- part4_extra_requests_by_reviewers.Rmd: Codes used to analyse the distribution of TCGA subgroups inside our risk-specific subgroups.
+- ucec_tcga_clinical_data.zip: Raw clinical data, downloaded from cBioportal.
+- uterus_rnaseq_VST1.z01, uterus_rnaseq_VST1.z02, uterus_rnaseq_VST1.zip, uterus_rnaseq_VST_G2.zip: These zip files contain the output of       part1_R_prepare_data.Rmd and the input of part2_Python_ML.py
+