Automated analysis of cystoscopic images may support consistent characterization of bladder tissue patterns, but reported performance is highly sensitive to patient composition, data partitioning, and evaluation methodology. Objective: This study provides an exploratory comparison of convolutional neural networks, Tran...
H. Kurt, Sabire Kılıçarslan, M. M. Çiçekliyurt et al.· International Journal of Mol...· 0 citations
These findings support a broader multi-gene candidate signature in prostate adenocarcinoma, in which machine learning prioritized genes such as XAF1, APP, RPA3, IFIH1, UBE2D2, RSAD2, KIF2C, and PLK1, while STAT1 and PLK1 provided complementary network-level biological relevance.
H. Kurt, Sabire Kılıçarslan, M. M. Çiçekliyurt et al.· International Journal of Mol...· 0 citations
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