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 currently also holds 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 regularly serves as a reviewer for BBSRC Responsive Mode and MRC Responsive Mode grants. He serves as a Program Committee member for top AI and Mathematical Modelling conferences, and as a reviewer for top Computational Biology journals, including Nature Protocols, Metabolic Engineering, Bioinformatics, Oncotarget, Briefings in Bioinformatics, PLOS Computational Biology, BMC Bioinformatics.
Summary of Research Interests
- Multi-omic models of cell metabolism
- Machine and deep learning
- Cancer metabolism
- Systems biology of genome-scale models
- Multi-objective optimization
- Living organisms as Turing machines
Education/Academic qualification
PhD, University of Cambridge
External positions
Visiting Professor, King Mongkut's University of Technology Thonburi
2019Visiting Professor, University of Bari
2019Fingerprint
- 1 Similar Profiles
Network
Projects
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THYME: Teesside, Hull and York – Mobilising Bioeconomy Knowledge Exchange
Montague, G., Zeng, Y., Angione, C. & Archer, G.
1/04/18 → 31/03/21
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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QuickFit: Quick fitting of prosthetic sockets for above knee amputees
Gao, J., Ali, Z., Angione, C. & Scott, S.
1/10/18 → 30/09/20
Project: Research
Research Output
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A hybrid flux balance analysis and machine learning pipeline elucidates the metabolic response of cyanobacteria to different growth conditions
Vijayakumar, S., Rahman, P-M. S. M. & Angione, C., 18 Nov 2020, In: iScience. 1 p., 101818.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth
Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C., 16 Jul 2020, In: Proceedings of the National Academy of Sciences. 11 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Developments in AI and Machine Learning for Neuroimaging
O’Sullivan, S., Jeanquartier, F., Jean-Quartier, C., Holzinger, A., Shiebler, D., Moon, P. & Angione, C., 24 Jun 2020, Developments in AI and Machine Learning for Neuroimaging. Holzinger, A., Goebel, R., Mengel, M. & Müller, H. (eds.). Springer, Vol. 12090. p. 307-320 14 p. (Lecture Notes in Computer Science).Research output: Chapter in Book/Report/Conference proceeding › Chapter
Open AccessFile -
Discovering Essential Multiple Gene Effects through Large Scale Optimization: an Application to Human Cancer Metabolism
Occhipinti, A., Hamadi, Y., Kugler, H., Wintersteiger, C., Yordanov, B. & Angione, C., 2 Apr 2020, In: IEEE/ACM Transactions on Computational Biology and Bioinformatics. 14 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Combining metabolic modelling with machine learning accurately predicts yeast growth rate
Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C., Jun 2019, (Accepted/In press).Research output: Contribution to conference › Paper › peer-review
Open AccessFile323 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. (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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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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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