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)
• MUH103/104: Occupational Health and Safety I & II (BSc in Computer 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)
• BSB651: Health Informatics (MSc in Health Informatics, Institute of Informatics, Hacettepe University)
• BSB652: Electronic Health Records and Coding (MSc in Health Informatics, Institute of Informatics, Hacettepe University)
• BIN503: Biological Databases & Data Analysis Tools (MSc in Bioinformatics, Department of Health Informatics, Graduate School of Informatics, METU)

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

• 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 winner (MSCA proposal 753397 – CROssBAR) in 2017
  
  
• TUBITAK, H2020 Programs, "Over the threshold" award in 2016 for scoring 91.2/100 in H2020-MSCA-IF-2016 proposal ESR with "CROssBAR" project.
  
  
• TUBITAK, H2020 Programs, "Over the threshold" award in 2015 for scoring 91.4/100 in H2020-MSCA-IF-2015 proposal ESR with "PaRTICLe" project.
  
  
• Swiss Institute of Bioinformatics (SIB) and UNIL, OMA Database Research Visit Fellowship in 2016.

1. 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” (https://www.ebi.ac.uk/)
   
   
2. 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/)
   
   
3. 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” (https://www.ebi.ac.uk/)
   
   
4. 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)
   
   
5. HIBIT-2021: 14th International Symposium on Health Informatics & Bioinformatics (location: Bilkent University, Ankara, date: 10–11 Sept 2021)
   Seminar title: “AI-centric approaches for integrating, associating, and analyzing large-scale and heterogeneous biomedical data” (http://hibit2021.bilkent.edu.tr/)
   
   
6. 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)
   
   
7. 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/)
   
   
8. 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/)
   
   
9. Around 50 seminars at university departments (in Turkey), international and national conferences and workshops (location: face to face / virtual)
   

1. National scientific research project: TUBITAK-ARDEB 1001 Scientific and Technological Research Projects Support Program 2022
   Project number: 122E148
   Project title: "Molecular Function-Driven Automated Design of New Protein Sequences with Generative Deep Learning"
   Project role: PI
   Duration: 36 months (2022-2025)
   
   
2. National scientific research project: TUBITAK 2247-A National Leader Researchers Program 2020
   Project number: 120C123
   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)
   
   
3. National scientific research project: TUBITAK 3501 Career Support Program 2021
   Project number: 120E531
   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)
   
   
4. National scientific research project: National Health Institutes of Turkey (TUSEB) – Systems Biology and Bioinformatics Project Call 2019
   Project number: 3912
   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
   
   
5. National scientific research project: TÜBİTAK 1001 The Scientific and Technological Research Projects Funding Program Call 2021
   Project number: 121E208
   Project title: "Deep Learning Models for Virtual Screening Against Proteins with Few Bioactive Compound Data (Azderin)"
   Project role: co-PI
   Duration: 36 months (2021-2024)
   
   
6. 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: researcher/co-PI
   Project partners: Dr Günseli Bayram Akçapınar, Acıbadem University
   Duration: 24 months (2020-2022)
   
   
7. International scientific research project: British Council & TUBITAK - Newton / Kâtip Çelebi Institutional Links Program Call 2016
   Project number: 116E930
   Project title: "Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning (CROssBAR)"
   Project role: co-PI
   Project partners: Dr Volkan Atalay, METU; Dr Rengül Çetin-Atalay, METU ve Dr Maria Martin, EMBL-EBI, Cambridge, UK
   Duration: 24 months (2017-2020)
   
   
8. National scientific research project: TUBITAK 1003 – Special Areas R&D Project Support Call 2018
   Project number: 318S218
   Project title: "Development of Gene Discovery and Drug Repositioning Platform Based on Machine Learning for Enhancing Immunotherapy Effectiveness in Cancer"
   Project role: researcher/co-PI
   Project partners: Dr Kemal Turhan, Dr Feriha Toksöz, Dr Emel Timuçin, Dr Zerrin Işık, Dr Burçin Kurt
   Duration: 36 months (2019-2022)
   
   
9. Scientific research fellowship (at United Kingdom): UniProt Database Development Grant
   Project title: "Novel Computational Approaches for Artificial Learning Based Drug Discovery and Repositioning"
   Project role: Post-doctoral fellow
   Project leader: Dr Maria Martin, EMBL-EBI, Cambridge, UK
   Duration: 24 months (2013-2015)

1. 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.
   
   
2. 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].
   
   
3. 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].
   
   
4. 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].
   
   
5. 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.
   
   
6. 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].
   
   
7. 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].
   
   
8. 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].
   
   
9. 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].
   
   
10. 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].
    
    
11. UniProt Consortium (2022). UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research, gkac1052 [doi:10.1093/nar/gkac1052].
    
    
12. 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.
    
    
13. Yakimovich, A., Özgür, A., Doğan, T., Ozkirimli, E. (2021). Machine Learning Methodologies to Study Molecular Interactions. Frontiers in Molecular Biosciences, 8, 1174.
    
    
14. 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.
    
    
15. 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.
    
    
16. UniProt Consortium. (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 49(D1), D480-D489.
    
    
17. 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.
    
    
18. 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].
    
    
19. 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.
    
    
20. 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.
    
    
21. 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.
    
    
22. 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.
    
    
23. 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.
    
    
24. UniProt Consortium. (2019). UniProt: a worldwide hub of protein knowledge. Nucleic acids research, 47(D1), D506-D515.
    
    
25. 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].
    
    
26. 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.
    
    
27. 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.
    
    
28. 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.
    
    
29. Doğan, T. (2018). HPO2GO: Prediction of Human Phenotype Ontology Term Associations For Proteins Using Cross Ontology Annotation Co-occurrences. PeerJ 6:e5298.
    
    
30. 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.
    
    
31. Pichler, K., et al. & UniProt Consortium. (2018). SPIN: Submitting Sequences Determined at Protein Level to UniProt. Current protocols in bioinformatics, e52.
    
    
32. Poux, S., et al. & UniProt Consortium. (2017). On expert curation and scalability: UniProtKB/Swiss-Prot as a case study. Bioinformatics, 33(21), 3454-3460.
    
    
33. 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.
    
    
34. UniProt Consortium. (2017). UniProt: the universal protein knowledgebase. Nucleic acids research, 45(D1), D158-D169.
    
    
35. 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.
    
    
36. 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].
    
    
37. Breuza, L., et al. & UniProt Consortium. (2016). The UniProtKB guide to the human proteome. Database, bav120.
    
    
38. UniProt Consortium. (2015). UniProt: a hub for protein information. Nucleic Acids Research, 43(D1): D204-D212.
    
    
39. 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.
    
    
40. UniProt Consortium. (2014). Activities at the Universal Protein Resource (UniProt). Nucleic Acids Research, 42(D1), D191-D198 [Corrigendum: doi:10.1093/nar/gku469].
    
    
41. 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. Ünlü, A., Çevrim, E., Sarıgün, A., Yigit, M.G., Çelikbilek, H., Bayram, O., Güvenilir, H.A., Koyaş, A., Kahraman, D.C., Olğaç, A., Rifaioğlu, A., Banoglu, E., & Doğan, T. (2023). Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks. arXiv preprint arXiv:2302.07868 [currently under review in a peer-reviewed journal].
   
   
2. Ulusoy, E. & Doğan, T. (2022). Mutual Annotation-Based Prediction of Protein Domain Functions with Domain2GO. bioRxiv, 2022.11.03.514980 [currently under review in a peer reviewed journal].
   
   
3. 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.), Methods in Molecular Biology (Vol. 1762), Computational Drug Discovery and Design, 1762:51-69 [DOI:10.1007/978-1-4939-7756-7_4],  ISBN: 978-1-4939-7755-0, Springer Nature.

1. 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.
   
   
2. 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. (2018). 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.
   
   
3. 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.
   
   
4. 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.
   
   
5. 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.
   
   
6. 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.
   
   
7. 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: 23^th Annual International Conference on Intelligent Systems for Molecular Biology, 10-14 July 2015, Dublin, Republic of Ireland.
   
   
8. 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 13^th European Conference on Computational Biology, 7-10 September 2014, Strasbourg, France.
   
   
9. Doğan, T. & Karaçalı, B. (2013). 2-D Thresholding of the Connectivity Map Following the Multiple Sequence Alignments of Diverse Datasets.The 10^th IASTED International Conference on Biomedical Engineering, 13-15 February 2013, Innsbruck, Austria. [doi:10.2316/P.2013.791-092].
   
   
10. Doğan, T. & Karaçalı, B. (2010). Evolutionary relationships between gene sequences via nonlinear embedding. 15. Ulusal Biyomedikal Mühendisliği Toplantısı (BIYOMUT), pp.1-4, 21-24 April 2010, Antalya, Turkey [doi:10.1109/BIYOMUT.2010.5479846].
    
    
11. 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.
    
    
12. 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: 25^th Annual International Conference on Intelligent Systems for Molecular Biology, 21-25 July 2017, Prague, Czechia.
    
    
13. 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.
    
    
14. 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: 23^th Annual International Conference on Intelligent Systems for Molecular Biology, 10-14 July 2015, Dublin, Republic of Ireland.
    
    
15. 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.org/10.1145/2808719.2811430].
    
    
16. Doğan, T., Bateman, A., & Martin, M. (2014). Weighted Pairwise Domain Architecture Alignment for Protein Homology Prediction.ECCB 2014: The 13^th European Conference on Computational Biology, 7-10 September 2014, Strasbourg, France.
    
    
17. Doğan, T. (2014). Multi-label Classification for Protein Function Prediction.Automated Function Prediction Meeting (AFP), ISMB 2014: 22^nd 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).
  • PC member of ""Function COSI at ISMB: Annual International Conference on Intelligent Systems for Molecular Biology" , 2017, 2018 & 2019 (http://biofunctionprediction.org/).
  • PC member of ""Proceedings of ISMB2022: 30th Annual International Conference on Intelligent Systems for Molecular Biology", 10-14 July 2022, Madison, USA (https://www.iscb.org/ismb2022).
  • PC member of ""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).
  • "HIBIT: International Symposium on Health Informatics and Bioinformatics", conferences, OC member on: 2022 & 2023, PC member on: 2018, 2019, 2020, 2021, 2022 & 2023.
  
  
• Journal editorship::
  
  • Associate editor in the Frontiers in Bioinformatics journal, Drug Discovery in Bioinformatics section (https://www.frontiersin.org/journals/bioinformatics) 2021-.
    
  • Guest editor in the Frontiers in Molecular Biosciences journal, 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:
  
  “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", "Nature Machine Intelligence", "Nature Communications", "PAMI"" journals and more.