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Machine and deep learning meet genome-scale metabolic modeling

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Machine and deep learning meet genome-scale metabolic modeling. / Zampieri, Guido; Vijayakumar, Supreeta; Yaneske, Elisabeth et al.

In: PLoS Computational Biology, Vol. 15, No. 7, e1007084, 11.07.2019, p. 1-24.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Zampieri, G, Vijayakumar, S, Yaneske, E & Angione, C 2019, 'Machine and deep learning meet genome-scale metabolic modeling', PLoS Computational Biology, vol. 15, no. 7, e1007084, pp. 1-24. https://doi.org/10.1371/journal.pcbi.1007084

APA

Zampieri, G., Vijayakumar, S., Yaneske, E., & Angione, C. (2019). Machine and deep learning meet genome-scale metabolic modeling. PLoS Computational Biology, 15(7), 1-24. [e1007084]. https://doi.org/10.1371/journal.pcbi.1007084

Vancouver

Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Computational Biology. 2019 Jul 11;15(7):1-24. e1007084. doi: 10.1371/journal.pcbi.1007084

Author

Zampieri, Guido ; Vijayakumar, Supreeta ; Yaneske, Elisabeth et al. / Machine and deep learning meet genome-scale metabolic modeling. In: PLoS Computational Biology. 2019 ; Vol. 15, No. 7. pp. 1-24.

Bibtex

@article{cceb3f0f189e4caf83c3bf2c7341301e,
title = "Machine and deep learning meet genome-scale metabolic modeling",
abstract = "Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.",
author = "Guido Zampieri and Supreeta Vijayakumar and Elisabeth Yaneske and Claudio Angione",
year = "2019",
month = jul,
day = "11",
doi = "10.1371/journal.pcbi.1007084",
language = "English",
volume = "15",
pages = "1--24",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "7",

}

RIS

TY - JOUR

T1 - Machine and deep learning meet genome-scale metabolic modeling

AU - Zampieri, Guido

AU - Vijayakumar, Supreeta

AU - Yaneske, Elisabeth

AU - Angione, Claudio

PY - 2019/7/11

Y1 - 2019/7/11

N2 - Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

AB - Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

U2 - 10.1371/journal.pcbi.1007084

DO - 10.1371/journal.pcbi.1007084

M3 - Journal article

VL - 15

SP - 1

EP - 24

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 7

M1 - e1007084

ER -