Author

Elisa Ferrari

Scuola Normale Superiore

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Genomics· 1

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Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:software / tool· 1 Organism:zebrafish· 1

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Alessandro CellerinoiD· 1 shared Kathrin Reichwald· 1 shared Marco GrothiD· 1 shared Mario BaumgartiD· 1 shared Philipp KochiD· 1 shared

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2022-2022 · peak 1/yr

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Alessandro CellerinoORCID

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Recent·Most cited

A deep neural network provides an ultraprecise transcriptomic clock of the short-lived fish Nothobranchius furzeri and identifies predictive genes translatable to human aging.

Elisa Ferrari, Kathrin Reichwald, Philipp Koch, Marco Groth, Mario Baumgart, Alessandro Cellerino

The paper develops a deep learning model called SA-DNN to create an ultraprecise transcriptomic aging clock in the short-lived fish N. furzeri and translates it to human aging with 3-year accuracy.

bioRxiv · Nov 26, 2022 · PreprintGenomics