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In this study, we propose a comparative study approach to understand how machine learning (ML) modeling using urinary biomarkers combined with demographic data can predict PDAC. The study also utilized a single-cell RNA sequencing (scRNA-seq) analysis to assess and understand gene expressions of included biomarkers. With inclusion of available biomarkers and incorporation of demographic information, we employed different approaches for preprocessing techniques, normalization approaches, ML techniques, and deep learning (DL) approaches to provide a comprehensive prediction model. The scRNA-seq approach also highlighted the significance of the urinary biomarkers from the pancreatic single-cell sample. Based on this analysis, the marker was identified as one of the top three most highly expressed genes in PDAC tissues. The predictive modeling approach was conducted for both binary and multiclass classification using both ML and DL approaches. The comparative analysis using all included parameter combinations produced modeling settings, and among these parameters, the DL modeling approach using binary classification outperformed the other approaches by achieving 91% accuracy. This framework provided insights that highlighted the critical role of demographic data and potential approaches to include such features in the model without impacting the predictive accuracy. Future work will focus on examining the framework using different datasets, integrating additional omics data, and exploring advanced DL architectures to further improve predictive performances.<\/jats:p>","DOI":"10.1093\/bib\/bbaf583","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:41:16Z","timestamp":1762789276000},"source":"Crossref","is-referenced-by-count":4,"title":["A machine learning framework using urinary biomarkers for pancreatic ductal adenocarcinoma prediction with\n                    <i>post hoc<\/i>\n                    validation via single-cell transcriptomics"],"prefix":"10.1093","volume":"26","author":[{"given":"Dahlak D","family":"Solomon","sequence":"first","affiliation":[{"name":"Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University , No. 301, Yuantong Road, Zhonghe District, New Taipei City 23561 ,","place":["Taiwan"]},{"name":"Yogananda School of AI Computers and Data Sciences, Shoolini University of Biotechnology and Management Sciences , Bajhol, Solan District, Himachal Pradesh 173229 ,","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Chung","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Chi-Mei Medical Center , No. 901, Chung Hwa Road, Yongkang District, Tainan City 71004 ,","place":["Taiwan"]},{"name":"Department of Health and Nutrition, Chia Nan University of Pharmacy and Science , No. 60, Section 1, Erren Road, Rende District, Tainan City 71710 ,","place":["Taiwan"]},{"name":"School of Medicine, College of Medicine, National Sun Yat-Sen University , No. 70, Lienhai Road, Gushan District, Kaohsiung City 80424 ,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsin-Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate Institute of Cancer Biology and Drug Discovery, College of 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Yuantong Road, Zhonghe District, New Taipei City 23561 ,","place":["Taiwan"]},{"name":"Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences , 1 Discovery Drive, Rensselaer, NY 12144 ,","place":["United States"]},{"name":"Cancer Center, Wan Fang Hospital, Taipei Medical University , No. 301, Yuantong Road, Zhonghe District, New Taipei City 23561 ,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui-Ru","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Medical Science and Technology, National Sun Yat-Sen University , 70 Lienhai Road, Gushan District, Kaohsiung City 80424 ,","place":["Taiwan"]},{"name":"Nursing Department, Kaohsiung Armed Forces General Hospital , 2 Zhongzheng 1st Road, Lingya District, Kaohsiung City 80284 ,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yung-Kuo","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of 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