<p>Estimate mutational signature exposure from sparse clinical sequencing data</p>

Arnab Chakrabarti; Hiroshi Hamano; Lancelot Seillier; Kjong-Van Lehmann

doi:10.7490/f1000research.1119967.1

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Chakrabarti A, Hamano H, Seillier L and Lehmann KV. Estimate mutational signature exposure from sparse clinical sequencing data [version 1; not peer reviewed]. F1000Research 2024, 13:1279 (poster) (https://doi.org/10.7490/f1000research.1119967.1)

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Estimate mutational signature exposure from sparse clinical sequencing data

Arnab Chakrabarti¹, Hiroshi Hamano, Lancelot Seillier, Kjong-Van Lehmann

Published 25 Oct 2024 (https://doi.org/10.7490/f1000research.1119967.1)

Author Affiliations

¹ Computational Biomedicine, RWTH Aachen University Medical Faculty, Aachen, North Rhine-Westphalia, Germany

Copyright © 2024 Chakrabarti A 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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Published 25 Oct 2024

Estimate mutational signature exposure from sparse clinical sequencing data
[version 1; not peer reviewed]

Arnab Chakrabarti¹, Hiroshi Hamano, Lancelot Seillier, Kjong-Van Lehmann

Author Affiliations

¹ Computational Biomedicine, RWTH Aachen University Medical Faculty, Aachen, North Rhine-Westphalia, Germany

Presented at

Intelligent Systems for Molecular Biology Conference (ISMB), 2025

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Mutational Signatures, Predictive Modeling, Cancer Genomics

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