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Personal profile

Academic Biography

Dr Claudio Angione joined Teesside University in 2015 as a Senior Lecturer in Computer Science.

Before joining Teesside, he worked at Microsoft Research in Cambridge and as a Research Associate at the University of Cambridge.

Claudio holds a PhD in Computer Science at the University of Cambridge, UK, 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 Applied 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.

His research interests are at the intersection of computer science, mathematics and biology. They include systems biology of genome-scale models, cancer metabolism, machine learning, multi-objective optimisation, computation with molecular Turing machines, and statistical-physical approaches in satisfiability problems.

Personal webpage: https://www.scm.tees.ac.uk/c.angione/

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

2019

Visiting Professor, University of Bari

2019

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

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Genes Engineering & Materials Science
Metabolic Networks and Pathways Medicine & Life Sciences
Genome Medicine & Life Sciences
Metabolism Engineering & Materials Science
Bacteria Engineering & Materials Science
Gene Expression Medicine & Life Sciences
Systems Biology Medicine & Life Sciences
Multiobjective optimization Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2016 2021

Research Output 2012 2019

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 conferencePaperResearchpeer-review

Open Access
File
Metabolic engineering
Gene expression
Yeast
Industrial applications
Learning systems

Human systems biology and metabolic modelling: a review - from disease metabolism to precision medicine

Angione, C., 10 Jun 2019, In : BioMed Research International.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Precision Medicine
Systems Biology
Metabolism
Medicine
Genes

Machine and deep learning meet genome-scale metabolic modelling

Zampieri, G., Vijayakumar, S., Yaneske, E. & Angione, C., 2 May 2019, (Accepted/In press) In : PLoS Computational Biology.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
artificial intelligence
Learning systems
Machine Learning
Genome
learning
5 Downloads (Pure)

Social Dynamics Modeling of Chrono-nutrition

Di Stefano, A., Scata, M., Vijayakumar, S., Angione, C., La Corte, A. & Lio, P., 30 Jan 2019, In : PLoS Computational Biology. 15, 1, e1006714.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Social Dynamics
human behavior
Nutrition
Human Behavior
Dynamic Modeling
12 Downloads (Pure)

CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design

Mancini, A., Eyassu, F., Conway, M., Occhipinti, A., Lió, P., Angione, C. & Pucciarelli, S., 30 Nov 2018, In : BMC Bioinformatics. 19, (Suppl 15), p. 442-442

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Metabolism
Research Design
Glucose
Metabolic Network
Prediction