Gün Kaynar

seq2ribo predicts ribosome location profiles from RNA sequences using a hybrid simulation and machine learning approach, achieving high correlation with experimental data.

CodonRL improves codon sequence optimization using reinforcement learning with structural feedback and expert demonstration, outperforming existing methods on multiple metrics.

LYCEUM is a machine learning-based CNV caller for ancient DNA that improves detection accuracy in low-coverage samples by pre-training on high-quality data and fine-tuning on limited aDNA samples.

ECOLE is a deep learning model for detecting copy number variants in whole exome sequencing data, achieving improved precision and recall over existing methods.

PiDeeL improves survival analysis and pathological classification of gliomas by integrating pathway information into a deep learning model, achieving better performance metrics than existing methods.