Mehmet Alper Yılmaz

ExactCN improves copy number variation estimation from whole-exome sequencing data using deep learning, achieving better accuracy than existing methods.

CHALLENGER improves CNV detection in challenging genomic regions using short-read WGS data, achieving higher F1-scores and capturing CNVs typically requiring long reads.

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.