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Claudio Angione

Showing results (21-30 of 67) with videos related to

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Briefings in Bioinformatics|June 3, 2017
Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modellingSupreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Cell Reports Methods|February 23, 2023
Metatranscriptomics-guided genome-scale metabolic modeling of microbial communitiesGuido Zampieri, Stefano Campanaro, Claudio Angione, et al.
Trends in Endocrinology and Metabolism: TEM|April 4, 2024
Emerging methods for genome-scale metabolic modeling of microbial communitiesChaimaa Tarzi, Guido Zampieri, Neil Sullivan, et al.
Proceedings of the National Academy of Sciences of the United States of America|July 18, 2020
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growthChristopher Culley, Supreeta Vijayakumar, Guido Zampieri, et al.
Iscience|December 23, 2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in CyanobacteriaSupreeta Vijayakumar, Pattanathu K S M Rahman, Claudio Angione
Trends in Cell Biology|December 13, 2023
Mechanism-aware and multimodal AI: beyond model-agnostic interpretationAnnalisa Occhipinti, Suraj Verma, Le Minh Thao Doan, et al.
FEBS Letters|August 19, 2021
Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy productionElisabeth Yaneske, Guido Zampieri, Loris Bertoldi, et al.
Plos One|September 17, 2015
Multi-Target Analysis and Design of Mitochondrial MetabolismClaudio Angione, Jole Costanza, Giovanni Carapezza, et al.
Molecular Biosystems|August 9, 2013
A design automation framework for computational bioenergetics in biological networksClaudio Angione, Jole Costanza, Giovanni Carapezza, et al.
Methods in Molecular Biology (Clifton, N.J.)|May 23, 2022
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic ModelingSupreeta Vijayakumar, Giuseppe Magazzù, Pradip Moon, et al.
Pageof 7

Showing results (21-30 of 67) with videos related to

Sort By:
Pageof 7
Briefings in Bioinformatics|June 3, 2017
Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modellingSupreeta Vijayakumar, Max Conway, Pietro Lió, et al.
Cell Reports Methods|February 23, 2023
Metatranscriptomics-guided genome-scale metabolic modeling of microbial communitiesGuido Zampieri, Stefano Campanaro, Claudio Angione, et al.
Trends in Endocrinology and Metabolism: TEM|April 4, 2024
Emerging methods for genome-scale metabolic modeling of microbial communitiesChaimaa Tarzi, Guido Zampieri, Neil Sullivan, et al.
Proceedings of the National Academy of Sciences of the United States of America|July 18, 2020
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growthChristopher Culley, Supreeta Vijayakumar, Guido Zampieri, et al.
Iscience|December 23, 2020
A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in CyanobacteriaSupreeta Vijayakumar, Pattanathu K S M Rahman, Claudio Angione
Trends in Cell Biology|December 13, 2023
Mechanism-aware and multimodal AI: beyond model-agnostic interpretationAnnalisa Occhipinti, Suraj Verma, Le Minh Thao Doan, et al.
FEBS Letters|August 19, 2021
Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy productionElisabeth Yaneske, Guido Zampieri, Loris Bertoldi, et al.
Plos One|September 17, 2015
Multi-Target Analysis and Design of Mitochondrial MetabolismClaudio Angione, Jole Costanza, Giovanni Carapezza, et al.
Molecular Biosystems|August 9, 2013
A design automation framework for computational bioenergetics in biological networksClaudio Angione, Jole Costanza, Giovanni Carapezza, et al.
Methods in Molecular Biology (Clifton, N.J.)|May 23, 2022
A Practical Guide to Integrating Multimodal Machine Learning and Metabolic ModelingSupreeta Vijayakumar, Giuseppe Magazzù, Pradip Moon, et al.
Pageof 7