Cross-Study Harmonization and Machine Learning Pipeline for Predicting Melanoma Treatment Response

We have 3 datasets The first one, from GEO with accession no. GSE75299, is https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75299. The second one, also from GEO with accession no. GSE65185, is at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi. The third one, from EGA with study ID EGAD00001001306, is at https://ega-archive.org/datasets/EGAD00001001306. purpose of the study Develop a machine learning framework for predicting treatment response in cutaneous melanoma patients by integrating and harmonising raw RNA-seq data from multiple independent studies. Methods, Software and version nf-core RNA-seq pipeline by nextflow (Feature count) R code (DEGs), ML, DL, Feature selection, Figures, validation