A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: its application on PAM50 algorithm | Zendy

Cristóbal Fresno | Zendy; Germán A. González | Zendy; Gabriela Merino | Zendy; Ana Georgina Flesia | Zendy; Osvaldo L. Podhajcer | Zendy; Andrea S. Llera | Zendy; Elmer A. Fernández | Zendy

Open Access

A novel non-parametric method for uncertainty evaluation of correlation-based molecular signatures: its application on PAM50 algorithm

Author(s) -

Cristóbal Fresno,

Germán A. González,

Gabriela Merino,

Ana Georgina Flesia,

Osvaldo L. Podhajcer,

Andrea S. Llera,

Elmer A. Fernández

Publication year - 2016

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/btw704

Subject(s) - computer science , parametric statistics , algorithm , correlation , software , data mining , mathematics , statistics , geometry , programming language

The PAM50 classifier is used to assign patients to the highest correlated breast cancer subtype irrespectively of the obtained value. Nonetheless, all subtype correlations are required to build the risk of recurrence (ROR) score, currently used in therapeutic decisions. Present subtype uncertainty estimations are not accurate, seldom considered or require a population-based approach for this context.

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