Projects per year
Personal profile
Academic Biography
Personal webpage: https://sites.google.com/view/angionelab/
Dr Claudio Angione is an Associate Professor in Computer Science at Teesside University, within the School of Computing, Engineering & Digital Technologies.
He leads the Turing Network Development at Teesside University, funded by an award from The Alan Turing Institute in 2022.
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, genome-scale cell models, statistical big data analytics. 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 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 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 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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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., Occhipinti, A. & Joneidy, S.
1/09/20 → 31/12/22
Project: Research
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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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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 AccessFile1 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 -
Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction
Pio, G., Mignone, P., Magazzù, G., Zampieri, G., Ceci, M. & Angione, C., 15 Jan 2022, In: Bioinformatics. 38, 2, p. 487-493 7 p.Research output: Contribution to journal › Article › peer-review
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Using Machine Learning as a Surrogate Model for Agent-Based Simulations
Angione, C., Silverman, E. & Yaneske, E., 10 Feb 2022, In: PLoS ONE. 17, 2, e0263150.Research output: Contribution to journal › Article › peer-review
Open AccessFile12 Downloads (Pure) -
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 AccessFile55 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. (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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Additional file 2 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.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
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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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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.10035344.v1, https://springernature.figshare.com/articles/of_Multiplex_methods_provide_effective_integration_of_multi-omic_data_in_genome-scale_models/10035344/1
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