Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types

FERNANDEZ-MUÑOZ, J M; CIOCCA, D R; GUERRERO-GIMENEZ, M E; HOLTON, K M; ZOPPINO, F C M; LANG, B J; CATANIA, C A; FERNANDEZ-MUÑOZ, J M; CIOCCA, D R; GUERRERO-GIMENEZ, M E; HOLTON, K M; ZOPPINO, F C M; LANG, B J; CATANIA, C A

BIOINFORMATICS (OXFORD, ENGLAND)

OXFORD UNIV PRESS

Año: 2020

1367-4803

Motivation: Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource togain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signaturesdo not exist for all cancer types, and most developed to date have been optimized for individual tumor types.In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patientsurvival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associatedwith patient survival.