<p>Decomprolute: a proteomic tumor deconvolution benchmarking framework</p>

Song Feng; Anna Calinawan; Francesca Petralia; Pei Wang; Pietro Pugliese; Michele Ceccarelli; Sara Gosline

doi:10.7490/f1000research.1119057.1

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Feng S, Calinawan A, Petralia F et al. Decomprolute: a proteomic tumor deconvolution benchmarking framework [version 1; not peer reviewed]. F1000Research 2022, 11:838 (poster) (https://doi.org/10.7490/f1000research.1119057.1)

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Decomprolute: a proteomic tumor deconvolution benchmarking framework

Song Feng, Anna Calinawan, Francesca Petralia, Pei Wang, Pietro Pugliese, Michele Ceccarelli, Sara Gosline¹

Published 26 Jul 2022 (https://doi.org/10.7490/f1000research.1119057.1)

Author Affiliations

¹ Pacific Northwest National Laboratory, USA

Copyright © 2022 Feng S 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 26 Jul 2022

Decomprolute: a proteomic tumor deconvolution benchmarking framework
[version 1; not peer reviewed]

Song Feng, Anna Calinawan, Francesca Petralia, Pei Wang, Pietro Pugliese, Michele Ceccarelli, Sara Gosline¹

Author Affiliations

¹ Pacific Northwest National Laboratory, USA

Presented at

Intelligent Systems for Molecular Biology (ISMB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

proteomics, deconvolution, CPTAC,

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