<p>Pipeline for applying deep learning models to predict gene expression in diverse inverted repeat lacking clade legumes</p>

Nicolas  Raymond; Jordan  Ubbens; Xi Zhang; Fatima  Daveoui; Ruchika  Verma; Dustin  Cram; Halim  Song; Yongguo  Cao; Morgan  Kirzinger; Sheikh  Jubair; David  Konkin

doi:10.7490/f1000research.1119811.1

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Raymond N, Ubbens J, Zhang X et al. Pipeline for applying deep learning models to predict gene expression in diverse inverted repeat lacking clade legumes [version 1; not peer reviewed]. F1000Research 2024, 13:878 (poster) (https://doi.org/10.7490/f1000research.1119811.1)

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Pipeline for applying deep learning models to predict gene expression in diverse inverted repeat lacking clade legumes

Nicolas Raymond, Jordan Ubbens, Xi Zhang¹, Fatima Daveoui, Ruchika Verma, Dustin Cram, Halim Song, Yongguo Cao, Morgan Kirzinger, Sheikh Jubair, David Konkin

Published 02 Aug 2024 (https://doi.org/10.7490/f1000research.1119811.1)

Author Affiliations

¹ ACRD-NRC, Canada

Copyright © 2024 Raymond N et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 02 Aug 2024

Pipeline for applying deep learning models to predict gene expression in diverse inverted repeat lacking clade legumes
[version 1; not peer reviewed]

Nicolas Raymond, Jordan Ubbens, Xi Zhang¹, Fatima Daveoui, Ruchika Verma, Dustin Cram, Halim Song, Yongguo Cao, Morgan Kirzinger, Sheikh Jubair, David Konkin

Author Affiliations

¹ ACRD-NRC, Canada

Presented at

Intelligent Systems for Molecular Biology Conference (ISMB), 2025

Abstract

Competing Interests

No competing interests were disclosed

Keywords

deep learning, legumes, pro-see, quant-seq

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