Personal profile
Academic Biography
Personal webpage: https://sites.google.com/view/angionelab/
Prof Claudio Angione is a Professor of Artificial Intelligence at Teesside University, within the School of Computing, Engineering & Digital Technologies.
He is the recipient of a Turing Network Development Award, funded by The Alan Turing Institute in 2022 and 2023.
He leads the Computational Systems Biology research group, and co-leads the Centre for Digital Innovation.
He also held two Visiting Professor positions at the University of Bari, in Italy, and at KMUTT, in Thailand.
He joined the university in 2015 as a Senior Lecturer, after a PostDoc at the University of Cambridge, and a research intern position at Microsoft Research UK.
He holds a PhD in Computer Science from the University of Cambridge, UK, awarded in 2015 with a thesis titled "Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science".
He previously obtained a Degree in Mathematics from the University of Catania, Italy. He also obtained a Higher Education Diploma from the Institute for Advanced Studies of the University of Catania, Italy.
Dr Angione's research group works at the intersection of computer science, mathematics and biology. Research topics include machine/deep learning, biomedical modelling and optimisation, systems biology, and genome-scale metabolic modelling. He has published more than 50 peer-reviewed papers, with recent projects led by Dr Angione being published in iScience (Cell Press), Bioinformatics, and PNAS. He has recently received several awards for his outstanding academic contributions, including an award from the Italian Embassy for the best research project in the Physical and Engineering Sciences in 2016.
He currently serves as Associate Editor for BMC Bioinformatics and Frontiers in Systems Biology, and as Editorial Board Member for BioMed Research International. He regularly serves as a reviewer for BBSRC and MRC Responsive Mode grants.
He also serves as a Program Committee member for top AI and Mathematical Modelling conferences, and as a reviewer for top Computational Biology journals, including Nature Methods, Nature Communications, PNAS, Nature Protocols, Cell Systems, Cell Reports, Metabolic Engineering, Bioinformatics, Oncotarget, Briefings in Bioinformatics, PLOS Computational Biology, BMC Bioinformatics.
Summary of Research Interests
- Deep learning
- Metabolic modelling
- Cancer metabolism
- Systems biology
- Genome-scale models
Education/Academic qualification
PhD, Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science, University of Cambridge
External positions
Turing Network Development Award Lead, Alan Turing Institute
2022 → 2023
Visiting Professor, King Mongkut's University of Technology Thonburi
2019
Visiting Professor, University of Bari
2019
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Collaborations and top research areas from the last five years
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EPSRC (NortHFutures) - Feasibility of Multimodal AI Integration of Imaging Data and Clinical Data to Predict Respiratory Outcomes in Preterm Infants
Occhipinti, A. (PI) & Angione, C. (CoI)
1/01/26 → 31/12/26
Project: Research
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Multimodal AI to predict patient-specific breast cancer treatment response
Occhipinti, A. (PI) & Angione, C. (CoI)
1/09/25 → 28/02/27
Project: Research
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A Comprehensive Network Against Brain Cancer
Li, X. (PI), Jancalek, R. (CoI), Kiskova-Simkova, T. (CoI), Pesic, M. (CoI), Hajdarpasic, A. (CoI), Golebiewska, A. (CoI), Bagci-Onder, T. (CoI), Angione, C. (CoI), Brander, S. (CoI), Toplu, A. (CoI), Booth, T. (CoI), Breznik, B. (CoI), Kaymaz, Y. (CoI), Frenkel-Morgenstern, M. (CoI), Athanasiou, A. (CoI), Mazo, C. (CoI) & Occhipinti, A. (CoI)
European Cooperation in Science and Technology (COST)
30/10/23 → 29/10/27
Project: Research
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KTP - CSX Carbon - Deep learning applications for peatland restoration
Occhipinti, A. (PI), Di Stefano, A. (CoI), Angione, C. (CoI) & Taylor, G. (CoI)
1/09/23 → 31/01/26
Project: Research
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Novel approaches for the identification of alpha-synuclein modifications as potential biomarkers in melanoma
Philippou, P. F. (PI), Outeiro, T. F. (CoI), Khundakar, A. (CoI), Jennings, C. (CoI), Lenis, V. P. (CoI) & Angione, C. (CoI)
15/06/23 → 29/02/24
Project: Research
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A community reconstruction of Chinese hamster metabolism and structural systems biology elucidate metabolic rewiring in lactate-free CHO cells
Di Giusto, P., Choi, D.-H., Antonakoudis, A., Duraikannan, V. G., Craveur, P., Cowie, N. L., Ganapathy, T., Ramesh, K., Benavidez-López, S., Orellana, C. A., Jiménez, N. E., Dworkin, L. A., Morrissey, J., Marin de Mas, I., Strain, B., Valdez-Cruz, N. A., Trujillo-Roldán, M. A., Marzluf, J., Martínez, V. S. & Zehetner, L. & 21 others, , 15 Apr 2026, (E-pub ahead of print) In: Cell Systems. 101574.Research output: Contribution to journal › Article › peer-review
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Bioprocess optimisation via joint machine learning and metabolic modelling
Zampieri, G., Sandner, V., Verma, S., Kraemer, J., Lennon, C., Occhipinti, A., McCreath, G. & Angione, C., 1 Jul 2026, In: Metabolic Engineering. 96, p. 113-128 16 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile3 Downloads (Pure) -
Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging
Doan, L. M. T., Shahhosseini, K., Verma, S., Marefat, A., Locicero, G., Verma, S., Angione, C. & Occhipinti, A., 13 Feb 2026, In: Communications Engineering. 5, 17 p., 30.Research output: Contribution to journal › Review article › peer-review
Open AccessFile8 Downloads (Pure) -
COST action CA22103 Net4Brain: Pan-European network advancing translational research in brain cancer
Kiskova-Simkova, T., Simko, P., Pesic, M., Occhipinti, A., Breznik, B., Golebiewska, A., Athanasiou, A., Gazea, T., Hajdarpasic, A., Booth, T., Bagci-Onder, T., Kaymaz, Y., Angione, C., Ngameduru, U., Jancalek, R. & Li, X., 1 Jun 2026, In: Critical Reviews in Oncology/Hematology. 222, 10 p., 105260.Research output: Contribution to journal › Review article › peer-review
Open AccessFile23 Downloads (Pure) -
Development of a multi-epitope vaccine candidate against Sindbis virus through integrated immunoinformatics approaches and molecular dynamics simulations
Ira, N. I., Jaishee, N., Saha, A., Naidoo, D., Islam, S. T., Tani, T. H., Sharma, N. R., Anandraj, A., Lokman, S. M., Angione, C. & Roy, A., 1 Feb 2026, In: Computers in Biology and Medicine. 202, 18 p., 111456.Research output: Contribution to journal › Article › peer-review
Open AccessFile5 Downloads (Pure)
Datasets
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Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy
Polvikoski, T. M. (Creator), Angione, C. (Creator), Quesnel, A. (Creator), Coles, N. P. (Creator), Filippou, P. S. (Creator) & Islam, M. (Creator), figshare, 2024
DOI: 10.6084/m9.figshare.c.6582472.v1, https://springernature.figshare.com/collections/Glycosylation_spectral_signatures_for_glioma_grade_discrimination_using_Raman_spectroscopy/6582472/1
Dataset
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Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy
Islam, M. (Creator), Quesnel, A. (Creator), Coles, N. P. (Creator), Polvikoski, T. M. (Creator), Angione, C. (Creator) & Filippou, P. S. (Creator), figshare, 2024
DOI: 10.6084/m9.figshare.c.6582472, https://springernature.figshare.com/collections/Glycosylation_spectral_signatures_for_glioma_grade_discrimination_using_Raman_spectroscopy/6582472
Dataset
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of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Creator), Conway, M. (Creator) & Lió, P. (Contributor), figshare, 2019
DOI: 10.6084/m9.figshare.10035341.v1, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035341/1
Dataset
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of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Creator), Conway, M. (Creator) & Lió, P. (Contributor), figshare, 2019
DOI: 10.6084/m9.figshare.10035347.v1, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035347/1
Dataset
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Additional file 2 of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Creator), Conway, M. (Creator) & Lió, P. (Contributor), figshare, 2019
DOI: 10.6084/m9.figshare.10035338.v1, https://springernature.figshare.com/articles/Additional_file_2_of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035338/1
Dataset
Press/Media
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Teesside University joins collaborative data science network
10/06/23
1 item of Media coverage
Press/Media: Press / Media
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Teesside University wins tech funding from Alan Turing Institute
20/01/22
1 item of Media coverage
Press/Media: Press / Media
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AI to be used in fight against peatland degradation
Occhipinti, A., Di Stefano, A., Fayaz, J. & Angione, C.
3/11/23
1 item of Media coverage
Press/Media: Press / Media
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Teesside University joins Data Science and AI network
9/06/23
1 item of Media coverage
Press/Media: Press / Media