{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(1)"],"submitter":["Zhang XJ"],"pubmed_abstract":["<h4>Background</h4>Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.<h4>Methods</h4>This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.<h4>Results</h4>Using multi-omics clustering, we discovered two cancer subtypes (CSs) associated with prognosis, among which CS1 exhibited poor prognostic outcomes. Subsequently, 13 central genes were identified through screening, constituting CMLS "],"journal":["Discover oncology"],"pagination":["96"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11775367"],"repository":["biostudies-literature"],"pubmed_title":["Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning."],"pmcid":["PMC11775367"],"pubmed_authors":["Guan KP","Zhang XJ","Lin FF","Wen YQ"],"additional_accession":[]},"is_claimable":false,"name":"Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning.","description":"<h4>Background</h4>Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.<h4>Methods</h4>This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.<h4>Results</h4>Using multi-omics clustering, we discovered two cancer subtypes (CSs) associated with prognosis, among which CS1 exhibited poor prognostic outcomes. Subsequently, 13 central genes were identified through screening, constituting CMLS ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2025-04-22T10:26:27.213Z","creation":"2025-04-05T23:34:13.024Z"},"accession":"S-EPMC11775367","cross_references":{"pubmed":["39873820"],"doi":["10.1007/s12672-025-01841-8"]}}