{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Grewal JK"],"funding":["Canada&apos;s Michael Smith Genome Sciences Centre","Foundation for the National Institutes of Health","National Cancer Institute","NCI NIH HHS","National Institutes of Health"],"pagination":["103705"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12013717"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["6(2)"],"pubmed_abstract":["We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection. For non-The Cancer Genome Atlas (TCGA) cancer samples, it provides subtype-level classification across 26 different TCGA cancer cohorts and 106 subtypes. For complete details on the use and execution of this protocol, please refer to Ellrott et al.<sup>1</sup>."],"journal":["STAR protocols"],"pubmed_title":["Protocol for obtaining cancer type and subtype predictions using subSCOPE."],"pmcid":["PMC12013717"],"funding_grant_id":["U24 CA264021","U24CA264023","U24 CA264009","U24 CA210990","U24CA264029","U24 CA264029","U24 CA264007","U24 CA264023"],"pubmed_authors":["Zenklusen JC","Tarnuzzer R","Lagani V","Lal A","Lee JA","Wang C","Drill E","Chagas VS","Uzunangelov V","Ferguson ML","Roh W","Charonyktakis P","Andersen JB","Akbani R","Gao GF","Shen R","Wang Z","Yau C","Yang L","Shmulevich I","Chiotti KE","Felau I","Hinoue T","Schultz A","Roberts LR","Cherniack AD","Hoyt SH","Caesar-Johnson SJ","Castro MAA","Karlberg BJ","Ojesina AI","Cancer Genome Atlas Analysis Network","Ellrott K","Lazar AJ","Robertson AG","Friedl V","Kemal A","Getz G","Ju Z","Stuart JM","Hoadley KA","Westlake L","Benz CC","Tsamardinos I","Wong CK","Grewal JK","Knijnenburg TA","Jones SJM","Lowenstein E","Apolonio VH","Broom BM","Spellman PT","Ramazzotti D","Tercan B","Struck A","Laird PW","Demchok JA","Kim TK"],"additional_accession":[]},"is_claimable":false,"name":"Protocol for obtaining cancer type and subtype predictions using subSCOPE.","description":"We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection. For non-The Cancer Genome Atlas (TCGA) cancer samples, it provides subtype-level classification across 26 different TCGA cancer cohorts and 106 subtypes. For complete details on the use and execution of this protocol, please refer to Ellrott et al.<sup>1</sup>.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-05-29T15:07:13.14Z","creation":"2025-07-03T03:05:10.55Z"},"accession":"S-EPMC12013717","cross_references":{"pubmed":["40202838"],"doi":["10.1016/j.xpro.2025.103705"]}}