<p>An algorithm for the efficient inference of cancer progression models</p>

Giulio Caravagna; Daniele Ramazzotti; Loes Olde Loohuis; Alex Graudenzi; Ilya Korsunsky; Luca De Sano; Andrea Paroni; Giancarlo Mauri; Bud Mishra; Marco Antoniotti

doi:10.7490/f1000research.1110109.1

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Caravagna G, Ramazzotti D, Olde Loohuis L et al. An algorithm for the efficient inference of cancer progression models [version 1; not peer reviewed]. F1000Research 2015, 4(ISCB Comm J):358 (poster) (https://doi.org/10.7490/f1000research.1110109.1)

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An algorithm for the efficient inference of cancer progression models

Giulio Caravagna¹, Daniele Ramazzotti, Loes Olde Loohuis, Alex Graudenzi, Ilya Korsunsky, Luca De Sano, Andrea Paroni, Giancarlo Mauri, Bud Mishra, Marco Antoniotti

Published 24 Jul 2015 (https://doi.org/10.7490/f1000research.1110109.1)

Author Affiliations

¹ University of Milan Bicocca, Italy

Copyright © 2015 Caravagna G 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 24 Jul 2015

An algorithm for the efficient inference of cancer progression models
[version 1; not peer reviewed]

Giulio Caravagna¹, Daniele Ramazzotti, Loes Olde Loohuis, Alex Graudenzi, Ilya Korsunsky, Luca De Sano, Andrea Paroni, Giancarlo Mauri, Bud Mishra, Marco Antoniotti

Author Affiliations

¹ University of Milan Bicocca, Italy

Presented at

23rd Annual International Conference on Intelligent Systems for Molecular Biology and the 14th European Conference on Computational Biology 2015

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Cancer Progression Inference, Selective Advantage, Next Generation Sequencing, Colorectal Cancer

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