Projects per year
Personal profile
Academic Biography
Personal webpage: https://www.scedt.tees.ac.uk/c.angione/
Dr Claudio Angione is a Reader in Computer Science at Teesside University, within the Department of Computer Science and Information Systems.
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, genome-scale cell models, statistical big data analytics. He has published more than 50 peer-reviewed papers in leading international conferences and high impact journals. He has recently received several awards for his outstanding academic contributions in the community, including an award by 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 as Editorial Board Member for BioMed Research International. He regularly serves as a reviewer for BBSRC Responsive Mode 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 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, University of Cambridge
External positions
Visiting Professor, King Mongkut's University of Technology Thonburi
2019
Visiting Professor, University of Bari
2019
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Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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KTP - The Clinkard Group Limited - Company Funding @ 50%
Angione, C., Qin, S. & 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 Computational Model of Cancer Metabolism for Personalised Medicine
Occhipinti, A. & Angione, C., 6 Mar 2021, Building Bridges in Medical Science 2021. Cambridge Medical Journal, (Cambridge Medical Journal; vol. 28 Mar).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile29 Downloads (Pure) -
An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications
Adegbenro, A., Short, M. & Angione, C., 8 Apr 2021, In: Energies. 14, 8, 18 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile15 Downloads (Pure) -
Dissecting the transcriptome in cardiovascular disease
Robinson, E. L., Baker, A. H., Brittan, M., McCracken, I., Condorelli, G., Emanueli, C., Srivastava, K. P., Gaetano, C., Thum, T., Vanhaverbeke, M., Angione, C., Heymans, S., Devaux, Y., Pedrazzini, T. & Martelli, F., 23 Mar 2021, In: Cardiovascular Research.Research output: Contribution to journal › Article › peer-review
Open AccessFile36 Downloads (Pure) -
Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy production
Yaneske, E., Zampieri, G., Bertoldi, L., Benvenuto, G. & Angione, C., 19 Aug 2021, (E-pub ahead of print) In: FEBS Letters.Research output: Contribution to journal › Article › peer-review
Open Access -
Integrated multi-omics analysis of ovarian cancer using variational autoencoders
Hira, M. T., Razzaque, M. A., Angione, C., Scrivens, J., Sawan, S. & Sarker, M., 18 Mar 2021, In: Nature Scientific Reports. 11, 1, 6265.Research output: Contribution to journal › Article › peer-review
Open AccessFile35 Downloads (Pure)
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.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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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.10035344.v1, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035344/1
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. (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