Triplet-based similarity score for fully multilabeled trees with poly-occurring labels | Zendy

Simone Ciccolella | Zendy; Giulia Bernardini | Zendy; Luca Denti | Zendy; Paola Bonizzoni | Zendy; Marco Previtali | Zendy; Gianluca Della Vedova | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Simone Ciccolella,

Giulia Bernardini,

Luca Denti,

Paola Bonizzoni,

Marco Previtali,

Gianluca Della Vedova

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa676

Subject(s) - similarity (geometry) , computer science , range (aeronautics) , tree (set theory) , measure (data warehouse) , mutation , data mining , cluster (spacecraft) , information retrieval , artificial intelligence , machine learning , biology , gene , mathematics , genetics , mathematical analysis , materials science , image (mathematics) , composite material , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research