Modeling transcriptomic age using knowledge-primed artificial neural networks

Modeling transcriptomic age using knowledge-primed artificial neural networks

Abstract The development of ‘age clocks’, machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current cloc...

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Bibliographic Details
Main Authors: Nicholas Holzscheck, Cassandra Falckenhayn, Jörn Söhle, Boris Kristof, Ralf Siegner, André Werner, Janka Schössow, Clemens Jürgens, Henry Völzke, Horst Wenck, Marc Winnefeld, Elke Grönniger, Lars Kaderali
Format: Article
Language:English
Published: Nature Publishing Group 2021-06-01
Series:npj Aging and Mechanisms of Disease
Online Access:https://doi.org/10.1038/s41514-021-00068-5

Internet

https://doi.org/10.1038/s41514-021-00068-5

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