Author

Birkan Gökbağ

The Ohio State UniversityORCID 0000-0002-9238-1650Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Approach:genetic perturbation / CRISPR· 1

Frequent Co-Authors

Lang LiiD· 2 shared Lijun ChengiD· 2 shared Anna Rychkova· 1 shared Daniel G. Stover· 1 shared Gregory NagyiD· 1 shared Isabel V. L. WilkinsoniD· 1 shared Jeffrey D. ParviniD· 1 shared Jens Christian SchwamborniD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersCancer Biology

Lang LiORCID

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Author profile →

3 papers

Recent·Most cited

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

Jun Yin, Maya L. Gosztyla, Birkan Gökbağ, Anna Rychkova, Isabel V. L. Wilkinson, John St John, Vega Shah, Sarah Nickels, Jens Christian Schwamborn, Robert T. Fremeau

The paper introduces scGENet, a framework to generate interpretable gene networks from single-cell foundation models, revealing conserved neurogenic dysfunction in Parkinson's disease.

bioRxiv · Jul 27, 2026 · PreprintNeuroscience

Identifying Novel Therapeutic Targets for Overcoming TNBC Chemo Resistance Through Comprehensive CRISPR-Cas9 Genome Screening

Shuai Shao, Shangjia Li, Yang Huo, Shan Tang, Birkan Gökbağ, Kunjie Fan, Yirui Huang, Lingling Wang, Gregory Nagy, Jeffrey D. Parvin, Daniel G. Stover, Lijun Cheng, Lang Li

This study uses CRISPR-Cas9 and RNAseq to identify novel synthetic lethal targets in TNBC cells resistant to cisplatin/doxorubicin, highlighting potential drug targets for combination therapies.

Cancers · Dec 3, 2025Peer-reviewedCancer Biology

DGCyTOF: deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data

Lijun Cheng, Pratik Karkhanis, Birkan Gökbağ, Yueze Liu, Lang Li

DGCyTOF improves cell type identification in single-cell mass cytometry data using deep learning and visualization, achieving high accuracy and intuitive clustering.

PLoS Computational Biology · Apr 11, 2022Peer-reviewedBioinformatics