Projects per year
Personal profile
Academic Biography
Personal webpage: https://sites.google.com/view/angionelab/
Dr 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
- Metabolic modelling
- Machine learning
- Deep learning
- Cancer metabolism
- Systems biology
- Genome-scale models
- Multi-objective optimization
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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Projects
- 16 Finished
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AMCH: Automated Morphological Characterisation of Hyphae (AMCH)
Juanes Ortiz, M. A., Angione, C., O'Toole, P. & Johnson, R.
1/07/21 → 30/04/22
Project: Research
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KTP - The Clinkard Group Limited - Company Funding @ 50%
Angione, C., Occhipinti, A. & Joneidy, S.
1/09/20 → 31/12/22
Project: Research
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Machine learning and metabolic modelling of biofilms in the human gut
Angione, C. & Efthimiou, G.
30/05/19 → 29/05/20
Project: Research
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A pipeline and comparative study of 12 machine learning models for text classification
Occhipinti, A., Rogers, L. & Angione, C., 6 Apr 2022, (E-pub ahead of print) In: Expert Systems with Applications. 201, 117193.Research output: Contribution to journal › Article › peer-review
Open AccessFile31 Downloads (Pure) -
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling
Vijayakumar, S., Magazzu, G., Moon, P., Occhipinti, A. & Angione, C., 24 May 2022, Computational Systems Biology in Medicine and Biotechnology . Cortassa, S. & Aon, M. A. (eds.). Springer, Vol. 2399. p. 87-122Research output: Chapter in Book/Report/Conference proceeding › Chapter
File139 Downloads (Pure) -
Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods
Magazzù, G., Zampieri, G. & Angione, C., 1 Dec 2022, In: Computers in Biology and Medicine. 151, A, 18 p., 106244.Research output: Contribution to journal › Article › peer-review
Open AccessFile10 Downloads (Pure) -
Computational profiling of natural compounds as promising inhibitors against the spike proteins of SARS-CoV-2 wild-type and the variants of concern, viral cell-entry process, and cytokine storm in COVID-19
Kar, P., Saleh-E-In, M. M., Jaishee, N., Anandraj, A., Kormuth, E., Vellingiri, B., Angione, C., Rahman, P. K. S. M., Pillay, S., Sen, A., Naidoo, D., Roy, A. & Choi, Y. E., 27 Mar 2022, (E-pub ahead of print) In: Journal of Cellular Biochemistry.Research output: Contribution to journal › Article › peer-review
Open Access -
Genome Sequencing Variations in the Octodon degus, an Unconventional Natural Model of Aging and Alzheimer's Disease
Hurley, M. J., Urra, C., Garduno, B. M., Bruno, A., Kimbell, A., Wilkinson, B., Marino-Buslje, C., Ezquer, M., Ezquer, F., Aburto, P. F., Poulin, E., Vasquez, R. A., Deacon, R., Avila, A., Altimiras, F., Whitney Vanderklish, P., Zampieri, G., Angione, C., Constantino, G., Holmes, T. C., & 3 others, 30 Jun 2022, In: Frontiers in Aging Neuroscience. 14, p. 894994 894994.Research output: Contribution to journal › Article › peer-review
Open Access
Datasets
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of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Contributor), Conway, M. (Creator) & Liรณ, P. (Creator), figshare, 1 Jan 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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Additional file 1 of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Contributor), Conway, M. (Creator) & Liรณ, P. (Creator), figshare, 1 Jan 2019
DOI: 10.6084/m9.figshare.10035335.v1, https://springernature.figshare.com/articles/Additional_file_1_of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035335/1
Dataset
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of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Contributor), Conway, M. (Creator) & Liรณ, P. (Contributor), figshare, 1 Jan 2019
DOI: 10.6084/m9.figshare.10035350.v1, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035350/1
Dataset
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of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Contributor), Conway, M. (Creator) & Liรณ, P. (Creator), figshare, 1 Jan 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
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of Multiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C. (Contributor), Conway, M. (Creator) & Liรณ, P. (Creator), figshare, 1 Jan 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
Press / Media
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University bags AI funding from Alan Turing Institute
25/01/22
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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How Personalized Medicine Is Transforming Healthcare
Annalisa Occhipinti & Claudio Angione
5/04/22
1 item of Media coverage
Press/Media: Press / Media