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Claudio Angione
Claudio Angione
Professor of Artificial Intelligence, Teesside University
Verified email at tees.ac.uk - Homepage
Title
Cited by
Cited by
Year
Machine and deep learning meet genome-scale metabolic modelling
G Zampieri, S Vijayakumar, E Yaneske, C Angione
PLoS Computational Biology 15 (7), e1007084, 2019
1662019
Seeing the wood for the trees: a forest of methods for optimisation and omic-network integration in metabolic modelling
S Vijayakumar, M Conway, P Liˇ, C Angione
Briefings in Bioinformatics, 2017
155*2017
Robust design of microbial strains
J Costanza, G Carapezza, C Angione, P Liˇ, G Nicosia
Bioinformatics 28 (23), 3097-3104, 2012
642012
Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine
C Angione
BioMed Research International 2019, 2019
622019
Predictive analytics of environmental adaptability in multi-omic network models
C Angione, P Liˇ
Scientific reports 5, 2015
562015
Multiplex methods provide effective integration of multi-omic data in genome-scale models
C Angione, M Conway, P Liˇ
BMC bioinformatics 17 (4), 257-269, 2016
542016
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth
C Culley, S Vijayakumar, G Zampieri, C Angione
Proceedings of the National Academy of Sciences 117 (31), 18869-18879, 2020
442020
Integrated multi-omics analysis of ovarian cancer using variational autoencoders
MT Hira, MA Razzaque, C Angione, J Scrivens, S Sawan, M Sarker
Scientific reports 11 (1), 6265, 2021
322021
Situating agent-based modelling in population health research
E Silverman, U Gostoli, S Picascia, J Almagor, M McCann, R Shaw, ...
Emerging Themes in Epidemiology 18 (1), 1-15, 2021
282021
Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization
SS Kashaf, C Angione, P Liˇ
BMC systems biology 11 (1), 1-13, 2017
252017
Making life difficult for Clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization
SS Kashaf, C Angione, P Liˇ
BMC systems biology 11 (1), 1-13, 2017
252017
Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism
C Angione
Bioinformatics 34 (3), 494–501, 2018
242018
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria
S Vijayakumar, PKSM Rahman, C Angione
Iscience 23 (12), 101818, 2020
222020
The poly-omics of ageing through individual-based metabolic modelling
E Yaneske, C Angione
BMC Bioinformatics 19 (14), 415, 2018
212018
Bioinformatics Challenges and Potentialities in Studying Extreme Environments
C Angione, P Li˛, S Pucciarelli, B Can, M Conway, M Lotti, H Bokhari, ...
International Meeting on Computational Intelligence Methods forá…, 2016
21*2016
Using machine learning as a surrogate model for agent-based simulations
C Angione, E Silverman, E Yaneske
PLOS ONE 17 (2), e0263150, 2022
20*2022
Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives
S Vijayakumar, M Conway, P Liˇ, C Angione
Metabolic Network Reconstruction and Modeling: Methods and Protocols, 389-408, 2018
192018
Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism
F Eyassu, C Angione
Royal Society Open Science 4 (10), 170360, 2017
192017
A hybrid of metabolic flux analysis and bayesian factor modeling for multiomic temporal pathway activation
C Angione, N Pratanwanich, P Liˇ
ACS synthetic biology 4 (8), 880-889, 2015
192015
Modelling pyruvate dehydrogenase under hypoxia
F Eyassu, C Angione
19*
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