Fused lasso algorithm for Cox' proportional hazards and binomial logit models with application to copy number profiles.
This paper presents an efficient algorithm based on the combination of Newton Raphson and Gradient Ascent, for using the fused lasso regression method to construct a genome-based classifier. The characteristic structure of copy number data suggests that feature selection should take genomic location into account for producing more interpretable results for genome-based classifiers. The fused lasso penalty, an extension of the lasso penalty, encourages sparsity of the coefficients and their differences by penalizing the L1-norm for both of them at the same time, thus using genomic location. The major advantage of the algorithm over other existing fused lasso optimization techniques is its ability to predict binomial as well as survival response efficiently. We apply our algorithm to two publicly available datasets in order to predict survival and binary outcomes.
Epidemiology and Biostatistics, Vrije Universiteit Medical Center, Amsterdam, The Netherlands; Netherlands Bioinformatics Centre, Geert Grooteplein 28, 6525 GA Nijmegen, The Netherlands.
Nimisha
N
Chaturvedi
Renée X
RX
de Menezes
Jelle J
JJ
Goeman
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D018572:Disease-Free Survival; D018628:Gene Dosage; D006801:Humans; D009101:Multiple Myeloma / Q000453:epidemiology / Q000235:genetics; D016016:Proportional Hazards Models; D001749:Urinary Bladder Neoplasms / Q000453:epidemiology / Q000235:genetics
Double-loop cross-validation; Microarray; Penalized regression