Author

Ahmet Arda Ceylan

Bilkent University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 3 Research Focus:variant calling· 3 Approach:deep learning· 2 Assay:whole-genome sequencing· 2 Approach:machine learning (classical)· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

A. Ercüment ÇiçekiD· 3 shared Mehmet Alper YılmaziD· 3 shared Erfan FarhangKia· 1 shared Furkan Karademir· 1 shared Gün KaynariD· 1 shared M. Mert GencturkiD· 1 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

6 top collaborators · 12 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersBioinformatics

A. Ercüment ÇiçekORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

ExactCN: Predicting Exact Copy Numbers on Whole Exome Sequencing Data

Erfan FarhangKia, Ahmet Arda Ceylan, M. Mert Gencturk, Mehmet Alper Yılmaz, Furkan Karademir, A. Ercüment Çiçek

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

bioRxiv · Nov 24, 2025 · PreprintBioinformatics

CHALLENGER: Detecting Copy Number Variants in Challenging Regions Using Whole Genome Sequencing Data

Mehmet Alper Yılmaz, Ahmet Arda Ceylan, A. Ercüment Çiçek

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

bioRxiv · Nov 23, 2025 · PreprintBioinformatics

LYCEUM: Learning to call copy number variants on low coverage ancient genomes

Mehmet Alper Yılmaz, Ahmet Arda Ceylan, Gün Kaynar, A. Ercüment Çiçek

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.

Bioinformatics · Jul 1, 2025Peer-reviewedBioinformatics