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.

Wit, E., Pazira, H., Abegaz, F., Gonzalez, J., Augugliaro, L. (2016). Sparse relative risk survival modelling. In Proceedings of the 31st International Workshop on Statistical Modelling (pp. 333-338).

Sparse relative risk survival modelling

AUGUGLIARO, Luigi
2016-01-01

Abstract

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.
2016
Settore SECS-S/01 - Statistica
Wit, E., Pazira, H., Abegaz, F., Gonzalez, J., Augugliaro, L. (2016). Sparse relative risk survival modelling. In Proceedings of the 31st International Workshop on Statistical Modelling (pp. 333-338).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/235163
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