6533b81ffe1ef96bd1277796

RESEARCH PRODUCT

Sparse relative risk survival modelling

E. C WitH PaziraF AbegazJ GonzalezLuigi Augugliaro

subject

gene expression datarelative risk regression modelsurvival analysisparsityhigh dimensional datadifferential geometrydglarsSettore SECS-S/01 - Statistica

description

Cancer survival is thought to closed linked to the genimic constitution of the tumour. Discovering such signatures will be useful in the diagnosis of the patient and may be used for treatment decisions and perhaps even the development of new treatments. However, genomic data are typically noisy and high-dimensional, often outstripping the number included in the study. Regularized survival models have been proposed to deal with such scenary. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and (near) non-convex regularizer.

yearjournalcountryeditionlanguage
2016-01-01
http://hdl.handle.net/10447/235163