title: Sparse logistic regression with Lp penalty for biomarker identification.
abstract: In this paper, we propose a novel method for sparse logistic regression with non-convex regularization Lp (p <1). Based on smooth approximation, we develop several fast algorithms for learning the classifier that is applicable to high dimensional dataset such as gene expression. To the best of our knowledge, these are the first algorithms to perform sparse logistic regression with an Lp and elastic net (Le) penalty. The regularization parameters are decided through maximizing the area under the ROC curve (AUC) of the test data. Experimental results on methylation and microarray data attest the accuracy, sparsity, and efficiency of the proposed algorithms. Biomarkers identified with our methods are compared with that in the literature. Our computational results show that Lp Logistic regression (p <1) outperforms the L1 logistic regression and SCAD SVM. Software is available upon request from the first author.
authors.affiliation: University of Maryland, USA. zliu@umm.edu
authors.forename: Zhenqiu
authors.initials: Z
authors.lastname: Liu
authors.forename: Feng
authors.initials: F
authors.lastname: Jiang
authors.forename: Guoliang
authors.initials: G
authors.lastname: Tian
authors.forename: Suna
authors.initials: S
authors.lastname: Wang
authors.forename: Fumiaki
authors.initials: F
authors.lastname: Sato
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Meltzer
authors.forename: Ming
authors.initials: M
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000032:analysis*; D003106:Colon / Q000737:chemistry; D003110:Colonic Neoplasms / Q000737:chemistry / Q000235:genetics; D004938:Esophageal Neoplasms / Q000737:chemistry / Q000235:genetics; D020869:Gene Expression Profiling; D006801:Humans; D016015:Logistic Models; D020411:Oligonucleotide Array Sequence Analysis
chemical_list: D015415:Biomarkers