Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging

Jiahang Li; Martin Brenner; Iro Pierides; Barbara Wessner; Bernhard Franzke; Eva-Maria Strasser; Steffen Waldherr; Karl-Heinz Wagner; Wolfram Weckwerth

doi:10.1101/2024.08.27.609825

Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging

Jiahang Li, Martin Brenner, Iro Pierides, Barbara Wessner, Bernhard Franzke, Eva-Maria Strasser, Steffen Waldherr, Karl-Heinz Wagner (Corresponding author), Wolfram Weckwerth (Corresponding author)

Publications: Contribution to journal › Article

Original language	English
Journal	bioRxiv : the preprint server for biology
DOIs	https://doi.org/10.1101/2024.08.27.609825
Publication status	Published - 28 Aug 2024

Austrian Fields of Science 2012

302020 Gerontology
106044 Systems biology
303009 Nutritional sciences
303028 Sport science

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10.1101/2024.08.27.609825Licence: Unspecified

Cite this

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title = "Machine learning and data-driven inverse modeling of metabolomics unveil key process of active aging",

author = "Jiahang Li and Martin Brenner and Iro Pierides and Barbara Wessner and Bernhard Franzke and Eva-Maria Strasser and Steffen Waldherr and Karl-Heinz Wagner and Wolfram Weckwerth",

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AU - Li, Jiahang

AU - Brenner, Martin

AU - Pierides, Iro

AU - Wessner, Barbara

AU - Franzke, Bernhard

AU - Strasser, Eva-Maria

AU - Waldherr, Steffen

AU - Wagner, Karl-Heinz

AU - Weckwerth, Wolfram

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JO - bioRxiv : the preprint server for biology

JF - bioRxiv : the preprint server for biology

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