Enhancing the lasso approach for developing a survival prediction model based on gene expression data

DOI10.1155/2015/259474zbMath1344.92112DBLPjournals/cmmm/KanekoHH15OpenAlexW1586874143WikidataQ35683003 ScholiaQ35683003MaRDI QIDQ308770

Shuhei Kaneko, Chikuma Hamada, Akihiro Hirakawa

Publication date: 6 September 2016

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2015/259474

zbMATH Keywords

cross-validationleast absolute shrinkageselection operatortrue positive genes

Mathematics Subject Classification ID

Medical applications (general) (92C50) Genetics and epigenetics (92D10)

Uses Software

• uniCox

Cites Work

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• Survival prediction using gene expression data: a review and comparison
• Least angle regression. (With discussion)
• L₁Penalized Estimation in the Cox Proportional Hazards Model
• Univariate Shrinkage in the Cox Model for High Dimensional Data
• L 1-Regularization Path Algorithm for Generalized Linear Models
• Regularization and Variable Selection Via the Elastic Net

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