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Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels

Simone Ciccolella, Giulia Bernardini, Luca Denti, Paola Bonizzoni, View ORCID ProfileMarco Previtali, Gianluca Della Vedova
doi: https://doi.org/10.1101/2020.04.14.040550
Simone Ciccolella
1Department of Computer Systems and Communication, University of Milano-Bicocca, Milan, Italy
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  • For correspondence: simone.ciccolella@unimib.it
Giulia Bernardini
1Department of Computer Systems and Communication, University of Milano-Bicocca, Milan, Italy
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Luca Denti
1Department of Computer Systems and Communication, University of Milano-Bicocca, Milan, Italy
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Paola Bonizzoni
1Department of Computer Systems and Communication, University of Milano-Bicocca, Milan, Italy
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Marco Previtali
1Department of Computer Systems and Communication, University of Milano-Bicocca, Milan, Italy
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  • ORCID record for Marco Previtali
Gianluca Della Vedova
1Department of Computer Systems and Communication, University of Milano-Bicocca, Milan, Italy
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Abstract

The latest advances in cancer sequencing, and the availability of a wide range of methods to infer the evolutionary history of tumors, have made it important to evaluate, reconcile and cluster different tumor phylogenies.

Recently, several notions of distance or similarities have been proposed in the literature, but none of them has emerged as the golden standard. Moreover, none of the known similarity measures is able to manage mutations occurring multiple times in the tree, a circumstance often occurring in real cases.

To overcome these limitations, in this paper we propose MP3, the first similarity measure for tumor phylogenies able to effectively manage cases where multiple mutations can occur at the same time and mutations can occur multiple times. Moreover, a comparison of MP3 with other measures shows that it is able to classify correctly similar and dissimilar trees, both on simulated and on real data.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 14, 2020.
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Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels
Simone Ciccolella, Giulia Bernardini, Luca Denti, Paola Bonizzoni, Marco Previtali, Gianluca Della Vedova
bioRxiv 2020.04.14.040550; doi: https://doi.org/10.1101/2020.04.14.040550
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Triplet-based similarity score for fully multi-labeled trees with poly-occurring labels
Simone Ciccolella, Giulia Bernardini, Luca Denti, Paola Bonizzoni, Marco Previtali, Gianluca Della Vedova
bioRxiv 2020.04.14.040550; doi: https://doi.org/10.1101/2020.04.14.040550

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