<p>Machine learning identifies weak prognostic signal in colorectal polyp’s transcriptome</p>

Russell Hung; Simon Fisher; Ditte Andersen; Gerard Lynch; Noori Maka; Jennifer Hay; Jakub Jawny; William Sloan; Stephen McSorley; Joanne Edwards; Ian Poole

doi:10.7490/f1000research.1119210.1

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Hung R, Fisher S, Andersen D et al. Machine learning identifies weak prognostic signal in colorectal polyp’s transcriptome [version 1; not peer reviewed]. F1000Research 2022, 11:1324 (poster) (https://doi.org/10.7490/f1000research.1119210.1)

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Machine learning identifies weak prognostic signal in colorectal polyp’s transcriptome

Russell Hung¹, Simon Fisher, Ditte Andersen, Gerard Lynch, Noori Maka, Jennifer Hay, Jakub Jawny, William Sloan, Stephen McSorley, Joanne Edwards, Ian Poole

Published 15 Nov 2022 (https://doi.org/10.7490/f1000research.1119210.1)

Author Affiliations

¹ Canon Medical Research Europe, UK

Copyright © 2022 Hung R 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 15 Nov 2022

Machine learning identifies weak prognostic signal in colorectal polyp’s transcriptome
[version 1; not peer reviewed]

Russell Hung¹, Simon Fisher, Ditte Andersen, Gerard Lynch, Noori Maka, Jennifer Hay, Jakub Jawny, William Sloan, Stephen McSorley, Joanne Edwards, Ian Poole

Author Affiliations

¹ Canon Medical Research Europe, UK

Presented at

European Conference on Computational Biology (ECCB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Colorectal, polyp, INCISE, Machine learning, Transcriptomics

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