| Polishing copy number variant calls on exome sequencing data via deep learning F Özden, C Alkan, AE Çiçek Genome research 32 (6), 1170-1182, 2022 | 8 | 2022 |
| RNAGEN: A generative adversarial network-based model to generate synthetic RNA sequences to target proteins F Ozden, S Barazandeh, D Akboga, SS Tabrizi, UOS Seker, AE Cicek bioRxiv, 2023.07. 11.548246, 2023 | 6 | 2023 |
| Utrgan: Learning to generate 5’utr sequences for optimized translation efficiency and gene expression S Barazandeh, F Ozden, A Hincer, UOS Seker, AE Cicek bioRxiv, 2023.01. 30.526198, 2023 | 5 | 2023 |
| ECOLE: Learning to call copy number variants on whole exome sequencing data B Mandiracioglu, F Ozden, G Kaynar, MA Yilmaz, C Alkan, AE Cicek Nature Communications 15 (1), 132, 2024 | 3 | 2024 |
| DORMAN: Database of reconstructed MetAbolic networks F Ozden, MC Siper, N Acarsoy, T Elmas, B Marty, X Qi, AE Cicek IEEE/ACM transactions on computational biology and bioinformatics 18 (4 …, 2019 | 3 | 2019 |
| Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs F Özden, P Minary Nucleic Acids Research 52 (18), e87-e87, 2024 | 1 | 2024 |
| Learning to Generate 5’UTR Sequences for Optimized Ribosome Load and Gene Expression S Barazandeh, F Ozden, A Hincer, UOS Seker, AE Cicek | 1 | |
| ÉCOLE: Learning to call copy number variants on whole exome B Mandiracioglu, F Ozden, C Alkan, AE Ciçek | | |
