{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2(4)"],"submitter":["Grewal JK"],"pubmed_abstract":["<h4>Importance</h4>A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis.<h4>Objective</h4>To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses.<h4>Design, setting, and participants</h4>This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from "],"journal":["JAMA network open"],"pagination":["e192597"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6487574"],"repository":["biostudies-literature"],"pubmed_title":["Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers."],"pmcid":["PMC6487574"],"pubmed_authors":["Taylor MD","Gelmon K","Mungall AJ","Yip S","Ma Y","Moore R","Zhao Y","Jones M","Renouf D","Laskin J","Tessier-Cloutier B","Grewal JK","Gakkhar S","Lim H","Jones SJM","Marra M"],"additional_accession":[]},"is_claimable":false,"name":"Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers.","description":"<h4>Importance</h4>A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis.<h4>Objective</h4>To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses.<h4>Design, setting, and participants</h4>This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from ","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Apr","modification":"2025-05-18T11:08:49.859Z","creation":"2025-05-18T11:08:49.859Z"},"accession":"S-EPMC6487574","cross_references":{"pubmed":["31026023"],"doi":["10.1001/jamanetworkopen.2019.2597"]}}