Exploring interactions in high-dimensional genomic data: an overview of Logic Regression, with applications

被引：62

作者：

Ruczinski, I

Kooperberg, C

LeBlanc, ML

机构：

[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA

[2] Fred Hutchinson Canc Res Ctr, Div Publ Hlth Sci, Seattle, WA 98109 USA

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2004年 / 90卷 / 01期

基金：

美国国家卫生研究院;

关键词：

adaptive model selection; Boolean logic; binary variables; interactions; single nucleotide polymorphisms;

D O I：

10.1016/j.jmva.2004.02.010

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Logic Regression is an adaptive regression methodology mainly developed to explore high-order interactions in genomic data. Logic Regression is intended for situations where most of the covariates in the data to be analyzed are binary. The goal of Logic Regression is to find predictors that are Boolean (logical) combinations of the original predictors. In this article, we give an overview of the methodology and discuss some applications. We also describe the software for Logic Regression, which is available as an R and S-Plus package. (C) 2004 Elsevier Inc. All rights reserved.

引用

页码：178 / 195

页数：18

共 22 条

[1]

AARTS EHL, 1989, SIMULTED ANNEALING B

[2] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].

AKAIKE, H .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723

[3] SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivation [J].

Blewitt, Marnie E. ;

Gendrel, Anne-Valerie ;

Pang, Zhenyi ;

Sparrow, Duncan B. ;

Whitelaw, Nadia ;

Craig, Jeffrey M. ;

Apedaile, Anwyn ;

Hilton, Douglas J. ;

Dunwoodie, Sally L. ;

Brockdorff, Neil ;

Kay, Graham F. ;

Whitelaw, Emma .

NATURE GENETICS, 2008, 40 (05) :663-669

[4] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[5] Population genetics - making sense out of sequence [J].

Chakravarti, A .

NATURE GENETICS, 1999, 21 (Suppl 1) :56-60

[6] Bayesian CART model search [J].

Chipman, HA ;

George, EI ;

McCulloch, RE .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1998, 93 (443) :935-948

[7] New goals for the US Human Genome Project: 1998-2003 [J].

Collins, FS ;

Patrinos, A ;

Jordan, E ;

Chakravarti, A ;

Gesteland, R ;

Walters, L ;

Fearon, E ;

Hartwelt, L ;

Langley, CH ;

Mathies, RA ;

Olson, M ;

Pawson, AJ ;

Pollard, T ;

Williamson, A ;

Wold, B ;

Buetow, K ;

Branscomb, E ;

Capecchi, M ;

Church, G ;

Garner, H ;

Gibbs, RA ;

Hawkins, T ;

Hodgson, K ;

Knotek, M ;

Meisler, M ;

Rubin, GM ;

Smith, LM ;

Smith, RF ;

Westerfield, M ;

Clayton, EW ;

Fisher, NL ;

Lerman, CE ;

McInerney, JD ;

Nebo, W ;

Press, N ;

Valle, D .

SCIENCE, 1998, 282 (5389) :682-689

[8]

COX DR, 1972, J R STAT SOC B, V34, P187

[9] Combining biomarkers to detect disease with application to prostate cancer [J].

Etzioni, R ;

Kooperberg, C ;

Pepe, M ;

Smith, R ;

Gann, PH .

BIOSTATISTICS, 2003, 4 (04) :523-538

[10] COMPARISON OF A STANDARD REGIMEN (CHOP) WITH 3 INTENSIVE CHEMOTHERAPY REGIMENS FOR ADVANCED NON-HODGKINS-LYMPHOMA [J].

FISHER, RI ;

GAYNOR, ER ;

DAHLBERG, S ;

OKEN, MM ;

GROGAN, TM ;

MIZE, EM ;

GLICK, JH ;

COLTMAN, CA ;

MILLER, TP .

NEW ENGLAND JOURNAL OF MEDICINE, 1993, 328 (14) :1002-1006

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