Serdar Bozdag

Dr. Serdar Bozdag received his BS degree in Computer Engineering at Marmara University and Ph.D. degree in Computer Science at the University of California, Riverside. Prior to joining Marquette University, Dr. Bozdag was a postdoctoral fellow in National Cancer Institute at the National Institutes of Health. In 2012, he joined Marquette University as an Assistant Professor in the Department of Mathematics, Statistics and Computer Science. In 2015, he received the Way Klingler Young Scholar Award. In 2019, he received the NIH's prestigious Maximizing Investigators' Research Award (MIRA), a 5-year single-PI research grant. In 2020, he joined UNT as an Associate Professor in the Department of Computer Science and Engineering and the Department of Mathematics with an affiliation with the BioDiscovery Institute. Dr. Bozdag has served as a Program Committee member in several bioinformatics conferences including ISMB, ACM-BCB, RECOMB/ISCB Conference on Regulatory & Systems Genomics and the Great Lakes Bioinformatics Conference. He is an editorial board member of PLOS ONE and Cancer Informatics journals. Dr. Bozdag's research goal is to develop open source integrative computational tools to analyze high dimensional biological, clinical and environmental exposure datasets to infer context-specific gene regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response.

Bozdag Lab

Google Scholar

CURRENT RESEARCH INTERESTS

• Developing machine learning and network science methods to integrate multi-omics data
• Inferring gene regulatory interactions and modules
• Prioritization of disease- and trait-specific genes
• Precision medicine

FOR PROSPECTIVE GRADUATE STUDENTS

Apply to the Graduate Program in Computer Science & Engineering

Apply to the Graduate Program in Mathematics

CURRENT GRANT-FUNDED PROJECTS

• S. Bozdag (PI), "Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response," Sponsored by NIH (NIGMS, MIRA R35), Total Amount Awarded: $1,787,536. (August 2019 - June 2025).

RECENT SIGNIFICANT PUBLICATIONS

1. Kesimoglu ZN, Rifat JIM, Bozdag S. Computational inference of co-regulatory modules from transcription factors, MicroRNAs, and their targets using CanMod2. Scientific Reports. 2025 Apr 11;15(1):12521. PMCID: PMC11992115.
2. Talo, S. Bozdag, “ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model”. Great Lakes Bioinformatics (GLBIO 2025), accepted (proceedings to appear as a special issue in Bioinformatics Advances).
3. S. Ferreira, S. Pandey, J. Hemminger, S. Bozdag, and M. S. Antunes, “Early changes in microRNA expression in Arabidopsis plants infected with the fungal pathogen Fusarium graminearum,” PLOS ONE. 2025 Feb 6;20(2):e0318532. PMCID: PMC11801585
4. Talo M, Bozdag S. Top-DTI: Integrating Topological Deep Learning and Large Language Models for Drug Target Interaction Prediction. To appear (ISMB/ECCB 2025) (Acceptance rate 17.5%) csrankings.org conference for the bioinformatics field.
5. Ozdemir C, Olaimat MA, Bozdag S. A Dynamic Model for Early Prediction of Alzheimer’s Disease by Leveraging Graph Convolutional Networks and Tensor Algebra. Pacific Symposium in Biocomputing (PSB 2025). 2025;30:675–689. PMCID: PMC11649016
6. Olaimat MA, Bozdag S. CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings. arXiv; 2025. Available from: http://arxiv.org/abs/2501.18891
7. Bose and S. Bozdag, “Identifying cell lines across pan-cancer to be used in preclinical research as a proxy for patient tumor samples,” Nature Communications Biology, vol. 7, no. 1, pp. 1–16, Sep. 2024, doi: 10.1038/s42003-024-06812-3.
8. Olaimat MA, Bozdag S. TA-RNN: an Attention-based Time-aware Recurrent Neural Network Architecture for Electronic Health Records. (ISMB 2024) Bioinformatics, vol. 40, no. Supplement_1, pp. i169–i179, Jun. 2024, doi: 10.1093/bioinformatics/btae264  (Acceptance rate 18%) csrankings.org conference for the bioinformatics field.
9. Aghdam MA, Bozdag S, Saeed F. PVTAD: Alzheimer’s Disease Diagnosis Using Pyramid Vision Transformer Applied to White Matter of T1-Weighted Structural MRI Data. 2024 IEEE International Symposium on Biomedical Imaging (ISBI 2024) Athens, Greece, 2024, pp. 1-4, doi: 10.1109/ISBI56570.2024.10635541
10. Kesimoglu ZN, Bozdag S. Fusing multiplex heterogeneous networks using graph attention-aware fusion networks. Scientific Reports. Nature Publishing Group; 2024 Nov 24;14(1):29119.
11. Ozdemir C, Olaimat MA, Vashishath Y, Bozdag S, Initiative ADN. IGCN: Integrative Graph Convolutional Networks for Multi-modal Data. arXiv; 2024. Available from: http://arxiv.org/abs/2401.17612
12. Zitnik M, Li MM, Wells A, Glass K, Gysi DM, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline S, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. Bioinformatics Advances, vol. 4, no. 1, p. vbae099, 2024
13. Kesimoglu Z.N., Bozdag S. SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural Network,” NAR Genomics and Bioinformatics, vol. 5, no. 2, p. lqad063, Mar. 2023, doi: 10.1093/nargab/lqad063.
14. Sita Sirisha Madugula, Suman Pandey, Shreya Amalapurapu, and Serdar Bozdag. NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool. ACS Omega 2023 8 (23), 20379-20388 DOI: 10.1021/acsomega.3c00286
15. M Al Olaimat, J Martinez, F Saeed, S. Bozdag PPAD: A deep learning architecture to predict progression of Alzheimer’s disease. (ISMB/ECCB 2023) Bioinformatics, vol. 39, no. Supplement_1, pp. i149–i157, Jun. 2023, doi: 10.1093/bioinformatics/btad249 (Acceptance rate 17%) csrankings.org conference for the bioinformatics field.
16. Yang, M. A. Al-Duailij, S. Bozdag and F. Saeed, Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks, 2022 IEEE International Conference on Big Data (IEEE BigData 2022), Osaka, Japan, 2022, pp. 3131-3138, https://doi.org/10.1109/BigData55660.2022.10021070
17. Bose B, Moravec M, Bozdag S. Computing microRNA-gene interaction networks in pan-cancer using miRDriver. Scientific Reports. Nature Publishing Group; 2022 Mar 8;12(1):3717.