Tunca Doğan, PhD
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Faculty Member (Professor)
Department of Computer Science and Artificial Intelligence Engineering
Institute of Informatics, Health Informatics Department
Graduate School of Health Sciences, Bioinformatics Department
Hacettepe University, 06800 Ankara, Turkey
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Collaborating Research Associate
Protein Function Development Team (UniProt database),
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),
CB10 1SD Cambridge, UK
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Email:
tuncadogan@gmail.com
tuncadogan@hacettepe.edu.tr
Phone:
Work (Turkey) +90(312)2977193/115

Researcher accounts:

ORCID: http://orcid.org/0000-0002-1298-9763

Google Scholar: https://scholar.google.com/citations?hl=en&user=tHnMNPEAAAAJ&view_op=list_works&sortby=pubdate

WOS/Publons: https://www.webofscience.com/wos/author/record/B-5274-2017

Undergraduate:

• BBM101: Introduction to Programming I (Python) (BSc in Computer Engineering, Department of Computer Engineering, Hacettepe University)
• BBM103: Introduction to Programming Laboratory I (Python) (BSc in Computer Engineering, Department of Computer Engineering, Hacettepe University)
• BBM411: Fundamentals of Bioinformatics (BSc in Computer Engineering, Department of Computer Engineering, Hacettepe University)
• AIN411: Introduction to Bioinformatics (BSc in AI Engineering, Department of Computer Engineering, Hacettepe University)

Graduate:

• CMP611: Advanced Bioinformatics and Computational Biology (MSc/PhD in Computer Science and Engineering, Department of Computer Engineering, Hacettepe University)
• BIN781: Biological Databases and Data Analysis Tools (MSc/PhD in Bioinformatics, Institute of Health Sciences, Hacettepe University)
• BHI610: Introduction to Programming in Health Informatics (MSc in Health Informatics, Institute of Informatics, Hacettepe University)
• BSB651: Health Informatics (MSc in Health Informatics w/o thesis, Institute of Informatics, Hacettepe University)
• BSB652: Electronic Health Records and Coding (MSc in Health Informatics w/o thesis, Institute of Informatics, Hacettepe University)
• BIN503: Biological Databases & Data Analysis Tools (MSc in Bioinformatics, Department of Health Informatics, Graduate School of Informatics, METU)

• Data science: Artificial intelligence, generative AI, machine/deep learning, data/text mining, data integration/representation/visualization, language models, knowledge graphs, generative modeling, representation learning, network analysis and graph theory.
  
  
• Bioinformatics and Computational Biology: Biomedical data analysis, protein design, protein function prediction, biological databases and biological ontologies, systems biology, personalized medicine.
  
  
• Cheminformatics: Computational drug discovery, rational drug design, virtual screening, chemogenomics, drug-target interaction prediction, molecular property prediction.
  
  

• Izmir Biomedicine and Genome Center (IBG) – Science Medal 2022
  
  
• TUSEB Aziz Sancar Science & Incentive Awards 2022 – Research Incentive Award
  
  
• Bilim Akademisi (Science Academy) Young Scientist Awards Program (BAGEP) 2022
  
  
• METU Parlar Foundation Honor, Science & Incentive Awards 2021 – Research Incentive Award
  
  
• TÜBİTAK 2247-A National Leader Researchers Program 2020 winner
  
  
• Hacettepe Science and Incentive Awards 2019 – Research Incentive Award
  
  
• Marie Sklodowska-Curie Actions Seal of Excellence (MSCA proposal 753397 – CROssBAR) in 2017
  
  
• TUBITAK, H2020 Program, "Over the threshold" award in 2015 and 2016 with H2020-MSCA-IF-2015/2016 proposals: "PaRTICLe and CROssBAR".
  
  
• Swiss Institute of Bioinformatics (SIB) and UNIL, OMA Database Research Visit Fellowship in 2016.

1. METU Informatics Institute, the 5th Open Research Day (location: Ankara, Turkey, date: 6 December 2024)
   Seminar title: “Applications of Generative Artificial Intelligence in Health and Biology” (https://ii.metu.edu.tr/tr/duyuru/open-research-day-2024)
   
   
2. PEGS Europe: 16th Annual Protein and Antibody Engineering Summit 2024, Machine Learning Stream (location: Barcelona, Spain, date: 7 November 2024)
   Seminar title: “Multi-Modal Learning of Protein Properties” (https://www.pegsummiteurope.com/machine-learning-part2)
   
   
3. EMBO Practical Course 2023: Integrative Modelling of Protein Interactions (location: Izmir, date: 17–22 Sept 2023)
   Seminar title: “Machine learning for interaction and functional prediction” (https://meetings.embo.org/event/23-biomolecular-interactions)
   
   
4. EMBL-EBI External Seminars (location: Cambridge, UK, date: 27 September 2022)
   Seminar title: “Deep Learning-based Generative and Discriminative Modeling for Systems Biology and Drug Discovery”
   
   
5. Biorelate Conference Series: Knowledge Graph in Drug Discovery Part 3 (location: London, UK / virtual, date: 12 Jan. 2022)
   Seminar title: “Construction of a Knowledge Graph-Based Computational System (CROssBAR) to Make Sense out of Heterogeneous Biomedical Data” (https://www.biorelate.com/)
   
   
6. EMBL-EBI Industry Partnerships: Workshop on Machine Learning in Drug Discovery (location: Cambridge, UK, date: 20 Oct. 2018)
   Seminar title: “DEEPScreen: Drug-Target Interaction Prediction with Deep Convolutional Neural Networks Using Compound Images”
   
   
7. HIBIT-2021: 14th International Symposium on Health Informatics & Bioinformatics (location: Ankara / virtual, date: 10–11 Sep 2021)
   Seminar title: “AI-centric approaches for integrating, associating, and analyzing large-scale and heterogeneous biomedical data” (http://hibit2021.bilkent.edu.tr/)
   
   
8. TUPA2023: International Proteomics Congress // 5th National Proteomics Congress (location: Ankara, date: 13–14 Oct 2023)
   Seminar title: “Artificial Intelligence Driven Approaches in Protein Data Science” (https://tupa2023.org)
   
   
9. 9th Turkish Medical World Congress, Health Institutes of Türkiye (location: Istanbul, date: 19–22 Oct 2023)
   Seminar title: “Artificial Intelligence Approaches in Target-Focused Drug Candidate Molecule Design” (https://ttdk.tuseb.gov.tr/)
   
   
10. 9th International BAU Drug Design Congress (location: Istanbul, date: 29 Nov – 2 Dec 2023)
    Seminar title: “AI-driven prediction of molecular and drug-related properties via chemical language models” (https://drugdesign.bau.edu.tr/)
    
    
11. 75+ seminars at university departments, (inter)national conferences, workshops and symposiums (location: face-to-face / virtual)
    
    

1. National scientific research project: TUBITAK-ARDEB 1001 Scientific and Technological Research Projects Support Program 2024
   Project number/acronym: 124E639/ContVAR
   Project title: "High Precision Protein Function Prediction with Graph Contrastive Representation Learning"
   Project role: PI
   Project partners: Dr Ezgi Karaca, IBG; Dr Deniz Cansen Kahraman, METU
   Duration: 36 months (2025-2028)
   
   
2. National scientific research project: MareNostrum 5 Resource Pilot Access Project Call 2024
   Project code/acronym: TURMIND/MIND
   Project title: "Molecular Intelligence for Novel Discovery (MIND): A Universal Multi-Modal Foundation Model for Life’s Molecules"
   Project role: PI
   Duration: 12 months (2025-2026)
   
   
3. National scientific research project: TUBITAK-ARDEB 1001 Scientific and Technological Research Projects Support Program 2022
   Project number/acronym: 122E148/ProtGEN
   Project title: "Molecular Function-Driven Automated Design of New Protein Sequences with Generative Deep Learning"
   Project role: PI
   Project partners: Dr Ezgi Karaca, IBG; Dr Abdurrahman Olğaç, Gazi University
   Duration: 36 months (2022-2025)
   
   
4. National scientific research project: TUBITAK-BIDEB 2247-A National Leader Researchers Program 2020
   Project number/acronym: 120C123/DrugGEN
   Project title: "Disease Centric Large Scale De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks"
   Project role: PI
   Duration: 36 months (2021-2024)
   
   
5. National scientific research project: TUBITAK-ARDEB 3501 Career Support Program 2021
   Project number/acronym: 120E531/CROssBARv2
   Project title: "Integrative Representation and Deep Graph Learning Based Prediction of Complex and Heterogeneous Relationships in Biomolecular and Biomedical Data"
   Project role: PI
   Duration: 30 months (2021-2024)
   
   
6. National scientific research project: National Health Institutes of Turkey (TUSEB) – Systems Biology and Bioinformatics Project Call 2019
   Project number/acronym: 3912/DeepResponse
   Project title: "Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling".
   Project role: PI
   Project partners: Dr Deniz Cansen Kahraman, METU
   Duration: 24 months (2020-2022)
   
   
7. National scientific research project: TÜBİTAK 1001 The Scientific and Technological Research Projects Funding Program Call 2021
   Project number/acronym: 121E208/Azderin
   Project title: "Deep Learning Models for Virtual Screening Against Proteins with Few Bioactive Compound Data"
   Project role: co-PI
   Duration: 36 months (2021-2024)
   
   
8. National scientific research project: National Health Institutes of Turkey (TUSEB) – Systems Biology and Bioinformatics Project Call 2019
   Project number: 4002
   Project title: "Automated Antimicrobial Peptide Recognition using Deep Learning"
   Project role: co-PI
   Project partners: Dr Günseli Bayram Akçapınar, Acıbadem University (PI)
   Duration: 24 months (2020-2022)
   
   
9. International scientific research project: British Council & TUBITAK - Newton / Kâtip Çelebi Institutional Links Program Call 2016
   Project number/acronym: 116E930/CROssBAR
   Project title: "Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning (CROssBAR)"
   Project role: co-PI
   Project partners: Dr Volkan Atalay, METU (co-PI); Dr Rengül Çetin-Atalay, METU (co-PI) and Dr Maria Martin, EMBL-EBI, Cambridge, UK (co-PI)
   Duration: 24 months (2017-2020)
   
   
10. National scientific research project: TUBITAK 1003 – Special Areas R&D Project Support Call 2018
    Project number/acronym: 318S218
    Project title: "Development of Gene Discovery and Drug Repositioning Platform Based on Machine Learning for Enhancing Immunotherapy Effectiveness in Cancer"
    Project role: co-PI
    Project partners: Dr Kemal Turhan, KTU (PI); Dr Emel Timuçin, Acıbadem University (co-PI), Dr Zerrin Işık, DEU (co-PI), Dr Yasemin Başbınar, DEU (co-PI); Dr Aybar Can Acar, METU (co-PI); Dr Serbülent Ünsal
    Duration: 36 months (2019-2022)
    
    

1. Ünlü, A., Çevrim, E., Yigit, M.G., Sarıgün, A., Çelikbilek, H., Bayram, O., Kahraman, D.C., Olğaç, A., Rifaioğlu, A., Banoglu, E., & Doğan, T. (2025). Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks. Nature Machine Intelligence, 1-17 [doi:10.1038/s42256-025-01082-y][Available online without restrictions here].
   
   
2. Ünlü, A., Ulusoy, E., Yiğit, M. G., Darcan, M., & Doğan, T. (2025). Protein language models for predicting drug–target interactions: Novel approaches, emerging methods, and future directions. Current Opinion in Structural Biology, 91, 103017 [doi:10.1016/j.sbi.2025.103017].
   
   
3. Wang, Y., Wang, X., Doğan, T., Sam-Agudu, N. A., Al-Tawfiq, J. A., & Pan, Q. (2025). Mpox: disease manifestations and therapeutic development. Journal of Virology, e00152-25.
   
   
4. Ulusoy, E. & Doğan, T. (2024). Mutual annotation‐based prediction of protein domain functions with Domain2GO. Protein Science, 33(6), e4988 [doi:10.1002/pro.4988].
   
   
5. Binarci, B., Kilic, E. K., Doğan, T., Cetin Atalay, R., Kahraman, D. C., & Nacak Baytas, S. (2024). Design, synthesis, and evaluation of novel Indole‐Based small molecules as sirtuin inhibitors with anticancer activities. Drug Development Research, 85(7), e70008 [doi:10.1002/ddr.70008].
   
   
6. Shojaei, M., Mohammadvand, N., Doğan, T., Alkan, C., Cetin-Atalay, R., & Acar, A.C. (2024). An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data. Computers in Biology and Medicine, 169, 107810.
   
   
7. Yüksel, A., Ulusoy, E., Ünlü, A. & Doğan, T. (2023). SELFormer: Molecular Representation Learning via SELFIES Language Models. Machine Learning: Science and Technology, 4(2), 025035 [doi: 10.1088/2632-2153/acdb30].
   
   
8. Cankara, F. & Doğan, T. (2023). ASCARIS: Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations.  Computational and Structural Biotechnology Journal, 21, 4743-4758 [doi: 10.1016/j.csbj.2023.09.017].
   
   
9. Lobentanzer, S., Aloy, P., Baumbach, J., Bohar, B., Carey, V.J., Charoentong, P., Danhauser, K., Doğan, T., ... & Saez-Rodriguez, J. (2023). Democratizing knowledge representation with BioCypher. Nature Biotechnology, 1-4 [doi:10.1038/s41587-023-01848-y].
   
   
10. Atas Guvenilir, H., & Doğan, T. (2023). How to approach machine learning-based prediction of drug/compound–target interactions. Journal of Cheminformatics, 15(1), 1-36.
    
    
11. Dalkıran, A., Atakan, A., Rifaioğlu, A. S., Martin, M. J., Atalay, R. C., Acar, A., Doğan, T. & Atalay, V. (2023) Transfer Learning for Drug-Target Interaction Prediction. Bioinformatics, 39(Supplement_1), i103-i110. [doi: 10.1093/bioinformatics/btad234].
    
    
12. Ozdilek, A.S., Atakan, A., Ozsari, G., Acar, A., Atalay, M.V., Doğan, T. & Rifaioglu, A.S. (2023). ProFAB–Open Protein Functional Annotation Benchmark. Briefings in Bioinformatics, 24(2), bbac627 [doi:10.1093/bib/bbac627].
    
    
13. Ciray, F., & Doğan, T. (2022). Machine Learning-based Prediction of Drug Approvals Using Molecular, Physicochemical, Clinical Trial and Patent Related Features. Expert Opinion on Drug Discovery [doi:10.1080/17460441.2023.2153830].
    
    
14. Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., & Doğan, T. (2022). Learning Functional Properties of Proteins with Language Models. Nature Machine Intelligence, 4(3), 227-245 [Available online without restrictions here].
    
    
15. Özsarı, G., Rifaioglu, A. S., Atakan, A., Doğan, T., Martin, M. J., Atalay, R. C., & Atalay, V. (2022). SLPred: A Multi-view Subcellular Localization Prediction Tool for Multi-location Human Proteins. Bioinformatics, 38(17), 4226–4229 [Available as PDF without restrictions here].
    
    
16. UniProt Consortium (2022). UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research, gkac1052 [doi:10.1093/nar/gkac1052].
    
    
17. Doğan, T., Güzelcan, E. A., Baumann, M., Koyas, A., Atas, H., Baxendale, I., Martin, M., & Cetin-Atalay, R. (2021). Protein Domain-Based Prediction of Compound–Target Interactions and Experimental Validation on LM Kinases. PLOS Computational Biology, 17(11), e1009171.
    
    
18. Yakimovich, A., Özgür, A., Doğan, T., Ozkirimli, E. (2021). Machine Learning Methodologies to Study Molecular Interactions. Frontiers in Molecular Biosciences, 8, 1174.
    
    
19. Cetin-Atalay, R., Kahraman, D. C., Nalbat, E., Rifaioglu, A. S., Atakan, A., Donmez, A., Atas, H., Atalay, M.V., Acar, A.C., Doğan, T., (2021). Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools. Journal of Gastrointestinal Cancer, 1-11.
    
    
20. Doğan, T., Atas, H., Joshi, V., Atakan, A., Rifaioglu, A.S., Nalbat, E., Nightingale, A., Saidi, R., Volynkin, V., Zellner, H. Cetin-Atalay, R., Martin, M. J. & Atalay, V. (2021). CROssBAR: Comprehensive Resource of Biomedical Relations with Knowledge Graph Representations. Nucleic Acids Research, 49(16), e96-e96.
    
    
21. UniProt Consortium. (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 49(D1), D480-D489.
    
    
22. Wang, Y., Wang, Q., Huang, H., Huang, W., Chen, Y., McGarvey, P. B., ... & UniProt Consortium. (2021). A crowdsourcing open platform for literature curation in UniProt. PLoS biology, 19(12), e3001464.
    
    
23. Cichońska, A., Ravikumar, B., Allaway, R.J., …, The IDG-DREAM Drug-Kinase Binding Prediction Challenge Consortium, et al. (2021). Crowdsourced mapping of unexplored target space of kinase inhibitors. Nature Communications, 12, 3307 [doi:10.1038/s41467-021-23165-1].
    
    
24. Rifaioglu, A.S., Nalbat, E., Atalay, M.V., Martin, M.J., Cetin-Atalay, R. & Doğan, T. (2020). DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations. Chemical Science, 11(9), 2531-2557.
    
    
25. Rifaioglu, A. S., Cetin Atalay, R., Cansen Kahraman, D., Doğan, T., Martin, M., & Atalay, V. (2020). MDeePred: Novel Multi-Channel protein featurization for deep learning based binding affinity prediction in drug discovery. Bioinformatics, 37(5), 693-704.
    
    
26. Donmez, A., Rifaioglu, A. S., Acar, A., Doğan, T., Cetin-Atalay, R., & Atalay, V. (2020). iBioProVis: Interactive Visualization and Analysis of Compound Bioactivity Space. Bioinformatics, 36(14), 4227-4230.
    
    
27. Rifaioglu, A.S., Atas, H., R., Martin, M.J., Cetin‐Atalay, R., Atalay, M.V. & Doğan, T. (2019). Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Briefings in Bioinformatics, 20(5), 1878-1912.
    
    
28. Rifaioglu, A.S., Doğan, T., Martin, M.J., Cetin-Atalay, R. & Atalay, M.V. (2019). DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks. Scientific reports, 9(1), 7344.
    
    
29. UniProt Consortium. (2019). UniProt: a worldwide hub of protein knowledge. Nucleic acids research, 47(D1), D506-D515.
    
    
30. Zhou, N., Jiang, Y., Bergquist, T.R., Lee, A.J., Kacsoh, B.Z., Crocker, A.W., Lewis, K.A., Georghiou, G., Nguyen, H.N., Hamid, M.N., Davis, L., Doğan, T., et al. (2019). The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology, 20, 244 [doi:10.1186/s13059-019-1835-8].
    
    
31. Garcia, L., Bolleman, J., Gehant, S., Redaschi, N., Martin, M. & UniProt Consortium (2019). FAIR adoption, assessment and challenges at UniProt. Scientific Data, 6(1), 1-4.
    
    
32. Dalkiran, A., Rifaioglu, A.S., Atalay, M.V., Martin, M.J., Cetin‐Atalay, R. & Doğan, T. (2018). ECPred: A Tool for the Prediction of the Enzymatic Functions of Protein Sequences Based on the EC Nomenclature. BMC Bioinformatics, 19(1), 334.
    
    
33. Demirel, H.C., Doğan, T. & Tuncbag, N. (2018). A Structural Perspective on Modulation of Protein-Protein Interactions with Small Molecules. Current Topics in Medicinal Chemistry, 18(8):700-713.
    
    
34. Doğan, T. (2018). HPO2GO: Prediction of Human Phenotype Ontology Term Associations For Proteins Using Cross Ontology Annotation Co-occurrences. PeerJ 6:e5298.
    
    
35. Rifaioglu, A.S., Doğan, T., Saraç, Ö.S., Ersahin, T., Saidi, R., Atalay, M.V., Martin, M.J. & Cetin‐Atalay, R. (2018). Large‐scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants. Proteins: Structure, Function, and Bioinformatics, 86(2), 135-151.
    
    
36. Pichler, K., et al. & UniProt Consortium. (2018). SPIN: Submitting Sequences Determined at Protein Level to UniProt. Current protocols in bioinformatics, e52.
    
    
37. Poux, S., et al. & UniProt Consortium. (2017). On expert curation and scalability: UniProtKB/Swiss-Prot as a case study. Bioinformatics, 33(21), 3454-3460.
    
    
38. Zaru, R., et al. & UniProt Consortium. (2017). From the research laboratory to the database: the CaenorhUSAitis elegans kinome in UniProtKB. Biochemical Journal, 474(4), 493-515.
    
    
39. UniProt Consortium. (2017). UniProt: the universal protein knowledgebase. Nucleic acids research, 45(D1), D158-D169.
    
    
40. Doğan, T., MacDougall, A., Saidi, R., Poggioli, D., Bateman, A., O’Donovan, C., & Martin, M. J. (2016). UniProt-DAAC: Domain Architecture Alignment and Classification, a New Method for Automatic Functional Annotation in UniProtKB. Bioinformatics, 32(15): 2264-2271.
    
    
41. Jiang, Y., Oron, T. R., Clark, W. T., Bankapur, A. R., D'Andrea, D., Lepore, R., Funk, C.S. Kahanda, I., Verspoor, K.M., Ben-Hur, A., Koo, E., Penfold-Brown, D., Shasha, D., Youngs, N., Bonneau, R., Lin, A., Sahraeian, S.M, Martelli, P.L., Profiti, G., Casadio, R., Cao, R., Zhong, Z., Cheng, J., Altenhoff, A., Skunca, N., Dessimoz, C., Doğan, T., et al. (2016). An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biology, 17:184 [doi:10.1186/s13059-016-1037-6].
    
    
42. Breuza, L., et al. & UniProt Consortium. (2016). The UniProtKB guide to the human proteome. Database, bav120.
    
    
43. UniProt Consortium. (2015). UniProt: a hub for protein information. Nucleic Acids Research, 43(D1): D204-D212.
    
    
44. Ison, J., Rapacki, K., Ménager, H., Kalaš, M., Rydza, E., Chmura, P., Anthon, C., Beard, N., Berka, K., Bolser, D., Booth, T., Bretaudeau, A., Brezovsky, J., Casadio, R., Cesareni, G., Coppens, F., Cornell, M., Cuccuru, G., Davidsen, K., Della Vedova, G., Doğan, T., et al. (2015). Tools and data services registry: a community effort to document bioinformatics resources. Nucleic acids research, 44(D1), D38-D47.
    
    
45. UniProt Consortium. (2014). Activities at the Universal Protein Resource (UniProt). Nucleic Acids Research, 42(D1), D191-D198 [Corrigendum: doi:10.1093/nar/gku469].
    
    
46. Doğan, T. and Karaçalı, B. (2013). Automatic Identification of Highly Conserved Family Regions and Relationships in Genome Wide Datasets Including Remote Protein Sequences. PLoS ONE 8(9): e75458 [doi:10.1371/journal.pone.0075458].
    
    

1. Çevrim, E., Yiğit, M. G., Ulusoy, E., Yılmaz, A., & Doğan, T. (2025). A Benchmarking Platform for Assessing Protein Language Models on Function-related Prediction Tasks. In Lukasz Kurgan and Daisuke Kihara (Eds.), Protein Function Prediction: Methods and Protocols (pp. 241-268). New York, NY: Springer Nature [doi:10.1007/978-1-0716-4662-5_14].
   
   
2. Atas, H., Tuncbag, N. & Doğan, T. (2018). Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites. In Mohini Gore and Umesh B. Jagtap (Eds.), Computational Drug Discovery and Design (pp. 51-69). New York, NY: Springer Nature [doi:10.1007/978-1-4939-7756-7_4].
   
   
3. Ulusoy, E., & Doğan, T. (2025). ProtHGT: Heterogeneous Graph Transformers for Automated Protein Function Prediction Using Biological Knowledge Graphs and Language Models. bioRxiv preprint, 10.1101/2025.04.19.649272.
   
   
4. Turei, D., Schaul, J., Palacio-Escat, N., Bohar, B., Bai, Y., Ceccarelli, F., Çevrim, E., Daley, M., Darcan, M., Dimitrov, D., Doğan, T. ... & Saez-Rodriguez, J. (2025). OmniPath: integrated knowledgebase for multi-omics analysis. bioRxiv preprint, 10.1101/2025.09.11.675512.
   
   
5. Ünsal, S., Özdemir, S., Kasap, B., Kalaycı, M. E., Turhan, K., Doğan, T., & Acar, A. C. (2024). Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting. arXiv preprint arXiv:2412.08649.
   
   
6. Mirza, A., Alampara, N., Ríos-García, M., Abdelalim, M., Butler, J., Connolly, B., ... & Jablonka, K. M. (2025). ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models. arXiv preprint arXiv:2505.12534.
   
   

1. Şen, B., Ulusoy, E., Darcan, M., Ergün, M. & Doğan, T. (2025). CROssBARv2: A Unified Biomedical Knowledge Graph for Heterogeneous Data Representation and LLM-Driven Exploration. ISMB/ECCB 2025: 33rd Annual International Conference on Intelligent Systems for Molecular Biology, 20-24 July 2025, Liverpool, UK.
   
   
2. Taşdemir, A., Barlas, B., Olgac, A., Karaca, E. & Doğan, T. (2025). FlowProt: Classifier-Guided Flow Matching for Targeted Protein Backbone Generation in the de novo DNA Methyltransferase Family. ISMB/ECCB 2025: 33rd Annual International Conference on Intelligent Systems for Molecular Biology, 20-24 July 2025, Liverpool, UK.
   
   
3. Akkaya, M., Yanmaz, R., Yavuz, S., Joshi, V., Martin, M. & Doğan, T. (2025). Large Language Model Applications on the Uniprot Protein Sequence and Annotation Database. ISMB/ECCB 2025: 33rd Annual International Conference on Intelligent Systems for Molecular Biology, 20-24 July 2025, Liverpool, UK.
   
   
4. Ulusoy, E., & Doğan, T. (2025). ProtHGT: Heterogeneous Graph Transformers for Automated Protein Function Prediction Using Biological Knowledge Graphs and Language Models. ISMB/ECCB 2025: 33rd Annual International Conference on Intelligent Systems for Molecular Biology, 20-24 July 2025, Liverpool, UK.
   
   
5. Unsal, S., Ozdemir, S., Ozdinc, I., Bayrakli, A., Albayrak, M., Turhan, K., Doğan, T. & Acar, A.C. (2023). Holistic Protein Representation (HOPER): Few-Shot Protein Function Prediction with Multimodal Representation Learning. ISMB/ECCB 2023: 31st Annual International Conference on Intelligent Systems for Molecular Biology, 23-27 July 2023, Lyon, France.
   
   
6. Doğan, T., Joshi, V., Rifaioglu, A.S., Ataş, H., Sinoplu, E., Nighingale, A., Volynkin, V., Zellner, H., Saidi, R., Cetin-Atalay, R., Martin, M.J., & Atalay, M.V. (2019). CROssBAR: Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning. ISMB/ECCB 2019: 27th Annual International Conference on Intelligent Systems for Molecular Biology, 21-25 July 2019, Basel, Switzerland.
   
   
7. Rifaioglu, A.S., Atalay, M.V., Martin, M.J., Cetin-Atalay, R. & Doğan, T. (2018). DEEPScreen: Drug-Target Interaction Prediction with Deep Convolutional Neural Networks Using Compound Images. ISMB 2018: 26th Annual International Conference on Intelligent Systems for Molecular Biology, 6-10 July 2018, Chicago, USA.
   
   
8. Doğan, T. (2018). HPO2GO: Prediction of Human Phenotype Ontology Term Associations for Proteins Using Cross Ontology Annotation Co-occurrences. ISMB 2018: 26th Annual International Conference on Intelligent Systems for Molecular Biology, 6-10 July 2018, Chicago, USA.
   
   
9. Doğan, T., Rifaioglu, A.S., Saidi, R., Martin, M., Atalay, M.V. & Cetin-Atalay, R. (2018). Automated Negative Gene Ontology Based Functional Predictions for Proteins with UniGOPred. ISMB 2018: 26th Annual International Conference on Intelligent Systems for Molecular Biology, 6-10 July 2018, Chicago, USA.
   
   
10. Doğan, T., Akhan, E., Baumann, M., Nightingale, A., Baxendale, I., Martin, M. & Cetin-Atalay, R. (2017). Computational Prediction of Novel Drug Candidate Compound – Target Protein Interactions and Their Verification on PI3K/AKT/mTOR Signalling Pathway. GLBIO 2017: Great Lakes Bioinformatics Conference, 15-17 May 2017, Chicago, USA.
    
    
11. Rifaioğlu, A., Doğan, T., Saraç, Ö.S., Atalay, V., Martin, M. & Atalay, R. (2015). UniGOPred and ECPred: Automated Function Prediction Tools Based on A Combination of Different Classifiers. AFP-CAFA SIG, ISMB/ECCB 2015: 23th Annual International Conference on Intelligent Systems for Molecular Biology, 10-14 July 2015, Dublin, Republic of Ireland.
    
    
12. Doğan, T., Nightingale, A., Poggioli, D. & Martin, M. (2014). Drug-Target Predictions with the Combination of Protein Domain Mapping and Ligand Similarity Detection. Drug Development Workshop, ECCB 2014: The 13th European Conference on Computational Biology, 7-10 September 2014, Strasbourg, France.
    
    
13. Doğan, T. & Karaçalı, B. (2013). 2-D Thresholding of the Connectivity Map Following the Multiple Sequence Alignments of Diverse Datasets. The 10th IASTED International Conference on Biomedical Engineering, 13-15 February 2013, Innsbruck, Austria. [DOI:10.2316/P.2013.791-092].
    
    
14. Doğan, T. & Karaçalı, B. (2010). Evolutionary relationships between gene sequences via nonlinear embedding. 15th National Biomedical Engineering Meeting (BIYOMUT), pp.1-4, 21-24 April 2010, Antalya, Turkey [DOI:10.1109/BIYOMUT.2010.5479846].
    
    
15. Yiğit, M.G., & Doğan, T. (2024). Target-Specific Drug Candidate Molecule Generation with Latent Diffusion Models. ECCB 2024: 23rd European Conference on Computational Biology, 16-20 September 2024, Turku, Finland.
    
    
16. Ulusoy, E., & Doğan, T. (2024). Automated Protein Function Prediction Using Biological Knowledge Graphs and Heterogeneous Graph Transformers. ECCB 2024: 23rd European Conference on Computational Biology, 16-20 September 2024, Turku, Finland.
    
    
17. Ünlü, A., Çevrim, E., Yigit, M.G., Olğaç, A., & Doğan, T. (2024). Autoregressive Generation of Target-Centric Drug Candidates with Chemical and Protein Language Models. ECCB 2024: 23rd European Conference on Computational Biology, 16-20 September 2024, Turku, Finland.
    
    
18. Atas Guvenilir, H., & Doğan, T. (2023). HetCPI: Knowledge Graph-Centric Drug Discovery via Heterogeneous Graph Transformers. ISMB/ECCB 2023: 31st Annual International Conference on Intelligent Systems for Molecular Biology, 23-27 July 2023, Lyon, France.
    
    
19. Doğan, T., Saidi, R., Rifaioglu, A., Atalay, M.V., Cetin-Atalay, R. & Martin, M. (2017). Human Phenotype Ontology Prediction with the Utilization of Co-occurrences Between HPO terms and GO terms. ISMB/ECCB 2017: 25th Annual International Conference on Intelligent Systems for Molecular Biology, 21-25 July 2017, Prague, Czechia.
    
    
20. Rifaioğlu, A., Doğan, T., Sarac, Ö.S., Saidi, R., Atalay, V., Martin, M.J. & Atalay, R. (2017). UniGOPred: A Large Scale Automated GO Term Annotation System for UniProtKB. GLBIO 2017: Great Lakes Bioinformatics Conference, 15-17 May 2017, Chicago, USA.
    
    
21. Doğan, T., Ersahin, T., Rifaioglu, A., Poggioli, D., Nightingale, A., Martin, M. & Cetin-Atalay, R. (2015). Computational drug target prediction and validation in PI3K/AKT pathway. ISMB/ECCB 2015: 23th Annual International Conference on Intelligent Systems for Molecular Biology, 10-14 July 2015, Dublin, Republic of Ireland.
    
    
22. Rifaioglu, A. S., Doğan, T., & Can, T. (2015). Unsupervised identification of redundant domain entries in InterPro database using clustering techniques. In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 505-506), ACM, 9-12 September 2015, Atlanta, USA [DOI:10.1145/2808719.2811430].
    
    
23. Doğan, T., Bateman, A., & Martin, M. (2014). Weighted Pairwise Domain Architecture Alignment for Protein Homology Prediction. ECCB 2014: The 13th European Conference on Computational Biology, 7-10 September 2014, Strasbourg, France.
    
    
24. Doğan, T. (2014). Multi-label Classification for Protein Function Prediction. Automated Function Prediction Meeting (AFP), ISMB 2014: 22nd Annual International Conference on Intelligent Systems for Molecular Biology, 11-15 July 2014, Boston, USA.
    
    

• Scientific conferences:
  
  • Co-chair of HIBIT-2017: 10^th International Symposium on Health Informatics & Bioinformatics, June 28–30, 2017, METU NCC, Northern Cyprus (http://hibit2017.ii.metu.edu.tr).
  • "HIBIT: International Symposium on Health Informatics and Bioinformatics" conferences, OC member in 2022 & 2023, PC member in 2018, 2019, 2020, 2021, 2022, 2023, 2024 and 2025.
  • PC member of the Function COSI at ISMB: Annual International Conference on Intelligent Systems for Molecular Biology, 2017, 2018 & 2019 (http://biofunctionprediction.org/).
  • PC member of the Proceedings of ISMB2022: 30th Annual International Conference on Intelligent Systems for Molecular Biology, July 10–14, 2022, Madison, USA (https://www.iscb.org/ismb2022).
  • PC member of the MLCSB COSI at ISMB/ECCB 2023: 31st Annual International Conference on Intelligent Systems for Molecular Biology, 23–27 July 2023, Lyon, France (https://www.iscb.org/ismbeccb2023).
  • PC member of the Proceedings of ISMB2024: 32nd Annual International Conference on Intelligent Systems for Molecular Biology, July 12–16, 2024, Montreal, Canada (https://www.iscb.org/ismb2024).
  • PC member of the Proceedings of ISMB2025: 33rd Annual International Conference on Intelligent Systems for Molecular Biology, July 20–24, 2025, Liverpool, UK (https://www.iscb.org/ismb2025).
  
  
• Journal editorship:
  
  • Associate editor in BMC Bioinformatics, Springer Nature Group (https://bmcbioinformatics.biomedcentral.com/), 2024–.
  • Associate editor in Frontiers in Bioinformatics, Drug Discovery in Bioinformatics section (https://www.frontiersin.org/journals/bioinformatics), 2021–.
  • Guest editor in Frontiers in Molecular Biosciences, research topic: Machine Learning Methodologies to Study Molecular Interactions (http://www.frontiersin.org/research-topics/14119/machine-learning-methodologies-to-study-molecular-interactions), 2020–2021.
  
  
• Reviewer for:
  
  • “Nature Machine Intelligence”, “Nature Communications”, “PAMI”, “Information Sciences”, “Science Bulletin”, “Briefings in Bioinformatics”, “Bioinformatics”, “Chemical Science”, “PLOS Computational Biology”, “PROTEINS: Structure, Function, and Bioinformatics”, “BMC Bioinformatics”, “Scientific Reports”, “IEEE/ACM TCBB”, “iScience”, “Patterns”, “Chemical Communications”, “Genes”, “PAJES”, “PeerJ”, “PLOS ONE”, “RSC Advances”, “Molecular Biosystems” journals and more.
  
  
• Membership in scientific societies and boards:
  
  • Board member, The Scientific and Technological Research Council of Turkey (TÜBİTAK), International Cooperation Projects Research Support Group (UPAG), 2023–2024.
  • Board member, Hacettepe University Scientific Research Coordination Unit, Head of the Science and Engineering Faculty Projects Evaluation Committee, 2024–.
  • Member, International Society for Computational Biology (ISCB), 2014–.