{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ha CSR"],"funding":["Wilhelm Sander-Stiftung"],"pagination":["e0280399"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9879447"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(1)"],"pubmed_abstract":["<h4>Background</h4>The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve "],"journal":["PloS one"],"pubmed_title":["Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning."],"pmcid":["PMC9879447"],"funding_grant_id":["2018.022.1"],"pubmed_authors":["Strauch K","Slater EP","Marini F","Bartsch DK","Ha CSR","Muller-Nurasyid M","Hauck SM","Petrera A"],"additional_accession":[]},"is_claimable":false,"name":"Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.","description":"<h4>Background</h4>The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2026-07-14T22:11:46.651Z","creation":"2025-04-06T08:15:52.889Z"},"accession":"S-EPMC9879447","cross_references":{"pubmed":["36701413"],"doi":["10.1371/journal.pone.0280399"]}}