Следене
Claudio Angione
Claudio Angione
Professor of Artificial Intelligence, Teesside University
Потвърден имейл адрес: tees.ac.uk - Начална страница
Заглавие
Позовавания
Позовавания
Година
Machine and deep learning meet genome-scale metabolic modelling
G Zampieri, S Vijayakumar, E Yaneske, C Angione
PLoS Computational Biology 15 (7), e1007084, 2019
2842019
Seeing the wood for the trees: a forest of methods for optimisation and omic-network integration in metabolic modelling
S Vijayakumar, M Conway, P Lió, C Angione
Briefings in Bioinformatics, 2017
198*2017
Using machine learning as a surrogate model for agent-based simulations
C Angione, E Silverman, E Yaneske
PLOS ONE 17 (2), e0263150, 2022
121*2022
Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine
C Angione
BioMed Research International 2019, 2019
1052019
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
912021
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, 1-15, 2021
852021
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
842020
Predictive analytics of environmental adaptability in multi-omic network models
C Angione, P Lió
Scientific reports 5, 2015
752015
A pipeline and comparative study of 12 machine learning models for text classification
A Occhipinti, L Rogers, C Angione
Expert Systems with Applications 201, 117193, 2022
692022
Robust design of microbial strains
J Costanza, G Carapezza, C Angione, P Lió, G Nicosia
Bioinformatics 28 (23), 3097-3104, 2012
672012
Multiplex methods provide effective integration of multi-omic data in genome-scale models
C Angione, M Conway, P Lió
BMC bioinformatics 17 (4), 257-269, 2016
632016
A hybrid flux balance analysis and machine learning pipeline elucidates metabolic adaptation in cyanobacteria
S Vijayakumar, PKSM Rahman, C Angione
Iscience 23 (12), 2020
462020
Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities
G Zampieri, S Campanaro, C Angione, L Treu
Cell Reports Methods 3 (1), 2023
452023
Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction
G Pio, P Mignone, G Magazzù, G Zampieri, M Ceci, C Angione
Bioinformatics 38 (2), 487-493, 2022
372022
Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts
MRF Gosselin, V Mournetas, M Borczyk, S Verma, A Occhipinti, J Róg, ...
Elife 11, e75521, 2022
332022
In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production
A Occhipinti, F Eyassu, TJ Rahman, PKSM Rahman, C Angione
PeerJ 6, e6046, 2018
332018
Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism
F Eyassu, C Angione
Royal Society Open Science 4 (10), 170360, 2017
332017
Modelling pyruvate dehydrogenase under hypoxia
F Eyassu, C Angione
33*
Dissecting the transcriptome in cardiovascular disease
EL Robinson, AH Baker, M Brittan, I McCracken, G Condorelli, ...
Cardiovascular Research 118 (4), 1004-1019, 2022
322022
The poly-omics of ageing through individual-based metabolic modelling
E Yaneske, C Angione
BMC Bioinformatics 19 (14), 415, 2018
322018
Системата не може да изпълни операцията сега. Опитайте отново по-късно.
Статии 1–20