IDENTIFICATION AND GLOBAL INTERPRETATION OF POSSIBLE BIOMARKERS FOR THE DIAGNOSIS OF PANCREATIC CANCER USING EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inonu University
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Pancreatic cancer is a highly lethal malignancy with poor prognosis and limited early diagnosis methods. In this study, 60 serum samples (30 pancreatic cancer patients, 30 controls) were analyzed to identify potential biomarkers for early detection using machine learning. Proteomic data were obtained via glycoprotein enrichment and mass spectrometry, identifying 232 proteins. After preprocessing, 29 proteins were selected using the Elastic Net method. XGBoost, optimized with 10-fold cross-validation, classified pancreatic cancer with high performance (AUC=0.850, accuracy=0.833). The SHAP method identified P02750 (Leucine-rich alpha-2-glycoprotein), P02766 (Transthyretin), P01031 (Complement C5), and P02649 (Apolipoprotein E) as key proteins affecting cancer risk. These biomarkers may play a crucial role in early diagnosis and personalized treatment, but further validation in larger studies is required. © 2025, Inonu University. All rights reserved.
Açıklama
Anahtar Kelimeler
Biomarker, Explainable artificial intelligence, Pancreatic cancer, XGBoost
Kaynak
Journal of Inonu University Vocational School of Health Services
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
13
Sayı
1
