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Author

Cemil Çolak

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5 papers indexed here

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#protein folding Open access Oct 2026

CRISPR dependency profiles distinguish pediatric from adult cancer cell lines via explainable machine learning: Identification of GPX4 and proliferative genes as therapeutic targets

Pediatric and adult cancers diverge profoundly in their molecular architecture, yet whether these differences extend to genome-scale genetic dependencies remains poorly characterized, with prior studies examining single subtypes or individual algorithms rather than a systematic, interpretable comparison. To address t...

Abdulvahap Pınar, Cemil Çolak, A. Arslan · 0 citations
Open access 2026

Machine learning prediction of inpatient length of stay in hospitalised patients with diabetes

Aim: This study aimed to identify clinical, demographic, and pharmaceutical predictors of inpatient hospital length of stay (LOS) among diabetic patients using supervised machine learning classifiers, and to evaluate the comparative predictive performance of these models for LOS risk stratification. Materials and Metho...

F. Yagin, E. Guldogan, Cemil Çolak · 0 citations
Review Open access Oct 2026

Quantum Computing, Quantum Machine Learning, and Quantum Mechanical Principles in Medical Physics: A PRISMA 2020-Compliant Systematic Review

Objectives: The convergence of quantum computing, quantum machine learning, and quantum mechanical principles represents an emerging paradigm in medical physics. Classical methods encounter hard limits in treatment planning, molecular-scale drug simulation, and medical image interpretation. The four quantum phenomena s...

G. Zorlu, Cemil Çolak · 0 citations
Open access Sep 2026

A Reproducible and Explainable Deep Learning Framework for Dermoscopic Image Analysis in Skin Cancer

Background/Objectives: Cutaneous melanoma caused over 331,000 new cases and 58,000 deaths worldwide in 2022, with five-year survival falling from 99.4% in localised disease to 35.6% after distant metastasis. Most dermoscopic deep learning studies report headline accuracy without addressing data leakage, calibration, or...

Abdulvahap Pınar, F. H. Yagin, Cemil Çolak et al. · 0 citations
Open access 2026

An explainable artificial intelligence–based stacking ensemble for heart failure mortality prediction: A data-leakage-free clinical decision support approach

A data-leakage-free stacking ensemble model predicting mortality from baseline variables, targeting high sensitivity for clinical safety, and utilizing Explainable Artificial Intelligence (XAI) methods can significantly enhance clinician confidence in AI-assisted evaluations.

Fadime Erinci, E. Guldogan, Cemil Çolak · 0 citations

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