Projects per year
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
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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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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BBSRC - National Biofilm Innovation Centre
Occhipinti, A. (PI) & Angione, C. (CoI)
1/01/23 → 31/03/23
Project: Research
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AMCH: Automated Morphological Characterisation of Hyphae (AMCH)
Juanes Ortiz, M. A. (PI), Angione, C. (CoI), O'Toole, P. (CoI) & Johnson, R. (CoI)
1/07/21 → 30/04/22
Project: Research
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Ablation of the dystrophin Dp71f alternative C-terminal variant increases sarcoma tumour cell aggressiveness
Alnassar, N., Hajto, J., Rumney, R. M. H., Verma, S., Borczyk, M., Saha, C., Kanczler, J., Butt, A. M., Occhipinti, A., Pomeroy, J., Angione, C., Korostynski, M. & Górecki, D. C., 9 Jun 2024, (E-pub ahead of print) In: Human Molecular Genetics. ddae094.Research output: Contribution to journal › Article › peer-review
Open Access -
Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients
Verma, S., Magazzù, G., Eftekhari, N., Lou, T., Gilhespy, A., Occhipinti, A. & Angione, C., 8 Jul 2024, In: Cell Reports Methods. 4, 7, 100817.Research output: Contribution to journal › Article › peer-review
Open Access -
Data-driven analysis of crustal and subduction seismic environments using interpretation of deep learning-based generalized ground motion models
Fayaz, J., Astroza, R., Angione, C. & Medalla, M., 15 Mar 2024, In: Expert Systems with Applications. 238, 121731.Research output: Contribution to journal › Article › peer-review
Open AccessFile23 Downloads (Pure) -
Emerging methods for genome-scale metabolic modeling of microbial communities
Tarzi, C., Zampieri, G., Sullivan, N. & Angione, C., 3 Apr 2024, (E-pub ahead of print) In: Trends in Endocrinology and Metabolism. 35, 6, p. 533-548 16 p.Research output: Contribution to journal › Review article › peer-review
Open Access -
Microbiome alterations are associated with apolipoprotein E mutation in Octodon degus and humans with Alzheimer's disease.
Zampieri, G., Cabrol, L., Urra, C., Castro-Nallar, E., Schwob, G., Cleary, D., Angione, C., Deacon, R. M. J., Hurley, M. J. & Cogram, P., 24 Jul 2024, In: iScience. 27, 8, 110348.Research output: Contribution to journal › Article › peer-review
Open Access
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.10035344, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035344
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, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035347
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.10035353.v1, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035353/1
Dataset
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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How Personalized Medicine Is Transforming Healthcare
5/04/22
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
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