Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

Journal: International journal of medical informatics

PMID: 40157244

Abstract

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited.

Authors

Gian Maria Zaccaria
Nicola Altini

Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n.4, Bari 70126, Italy.
Valentina Mongelli

Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy.
Francescomaria Marino

Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona, 4, Bari 70126, Italy.
Vitoantonio Bevilacqua

Keywords

Carcinoma, Pancreatic Ductal CpG Islands DNA Methylation Epigenomics Female Humans Machine Learning Male Middle Aged Mutation Pancreatic Neoplasms Prognosis Proto-Oncogene Proteins p21(ras) Tumor Suppressor Protein p53

External Resources

View on PubMed Access via DOI PubMed (40157244)

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

Abstract

Authors

Keywords

External Resources

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