LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer - Babalyan, K.; Sultanov, R.; Generozov, E.; Sharova, E.; Kostryukova, E.; Larin, A.; Kanygina, A.; Govorun, V.; Arapidi, G. (PloS one, 2018)
Keywords: Adenocarcinoma; Algorithms; Bioinformatics; Biology and life sciences; Bladder; Bladder cancer; Chemistry; Colorectal cancer; Colorectal carcinoma; CpG Islands; Deoxyribonucleic acid; Diabetes; Diagnostic software; Diagnostic systems; DNA; DNA Methylation; Entropy (Information theory); Epigenetics; Gene Expression Regulation, Neoplastic; Genomes; Genomics; Heterogeneity; Humans; Kidney cancer; Kidneys; Learning algorithms; Machine Learning; Male; Medical diagnosis; Medicine; Medicine and Health Sciences; Models, Genetic; Neoplasms - diagnosis; Neoplasms - genetics; Panels; Pathogenesis; Physical Sciences; Physics; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - genetics; Rank tests; Source code; Tumors; Russia