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

Journal of Multivariate Analysis

Volumn 90, Issue 1 SPEC. ISS., 2004, Pages 178-195

  Ruczinski, Ingo ^a   Kooperberg, Charles ^b   LeBlanc, Michael L ^b  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b Fred Hutchinson Cancer Research Center   (United States)

Author keywords

Adaptive model selection; Binary variables; Boolean logic; Interactions; Single nucleotide polymorphisms

Indexed keywords

EID: 12744255343     PISSN: 0047259X     EISSN: 10957243     Source Type: Journal    
DOI: 10.1016/j.jmva.2004.02.010     Document Type: Article

References (22)

1. E.H.L. Aarts, J.H.M. Korst, Simulated Annealing and Boltzmann Machines, Wiley, New York, 1989.
2. H. Akaike, A new look at the statistical model identification, IEEE Trans. Ac. 19 (1974) 716-723.
3. L. Breiman, Random forests, Machine Learning 45 (2001) 5-32.
4. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth, Belmont, CA, 1984.
5. A. Chakravarti, Population genetics - making sense out of sequence, Nat. Gen. 21 (1999) S56-S60.
6. H. Chipman, E. George, R. McCulloch, Bayesian cart model search (with discussion), J. Amer. Statist. Assoc. 93 (1998) 935-960.
7. F.S. Collins, A. Patrinos, E. Jordan, A. Chakravarti, L.W.R. Gesteland, New goals for the US human genome project: 1998-2003, Science 282 (1998) 683-689.
8. D.R. Cox, Regression models and life tables (with discussion), J. Roy. Statist. Soc. B 34 (1972) 187-220.
9. R. Etzioni, C. Kooperberg, M. Pepe, R. Smith, P.H. Gann, Combining biomarkers to detect disease with application to prostate cancer, Biostatist. 4 (2003) 523-538.
10. R.I. Fisher, E.R. Gaynor, S. Dahlberg, M.M. Oken, E.M.M.T.M. Grogan, J.H. Glick, C.A. Coltman, T.P. Miller, Comparison of a standard regimen (chop) with three intensive chemotherapy regimens for advanced non-hodgkin's lymphoma, N. Engl. J. Med. 328 (1993) 1002-1006.
11. H. Fleischer, M. Tavel, J. Yeager, Exclusive-or representation of boolean functions, IBM J. Res. Dev. 25 (1983) 412-416.
12. J.H. Friedman, Multivariate adaptive regression splines (with discussion), Ann. Statist. 19 (1991) 1-141.
13. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, New York, 2001.
14. C. Kooperberg, I. Ruczinski, M. LeBlanc, L. Hsu, Sequence analysis using logic regression, Gen. Epi 21 (2001) S626-S631.
15. M. LeBlanc, J. Crowley, Survival trees by goodness of split, J. Amer. Statist. Assoc. 88 (1993) 457-467.
16. P.R. Lucek, J. Ott, Neural network analysis of complex traits, Gen. Epi. 14 (1997) S1101-S1106.
17. I. Ruczinski, Logic regression and statistical issues related to the protein folding problem, Ph.D. Thesis, University of Washington, Seattle, WA 98195, 2000.
18. I. Ruczinski, C. Kooperberg, M. LeBlanc, Logic regression, J. Comput. Graph. Statist. 12 (2003) 475-511.
19. The International Non-Hodgkin's Lymphoma Prognostic Factors Project, A predictive model for aggressive non-hodgkin's lymphoma, N. Engl. J. Med. 329 (1993) 987-994.
20. E.M. Wijsman, L. Almasy, C.I. Amos, I. Borecki, C.T. Falk, T.M. King, M.M. Martinez, D. Meyers, R. Neuman, J.M. Olson, S. Rich, M.A. Spence, D.C. Thomas, V.J. Vieland, J.S. Witte, J.W. MacCluer (Eds.), Analysis of complex genetic traits: Applications to asthma and simulated data, Gen. Epi. 21 (2001) (S1).
21. J.S. Witte, B.A. Fijal, Introduction: analysis of sequence data and population structure, Gen. Epi. 21 (2001) S600-S601.
22. R.Y.L. Zee, J. Hoh, S. Cheng, R. Reynolds, A.S.M A Grow, K. Walker, L. Steiner, A.F.-O.G Zangenberg, C. Macaya, E. Pintor, A. Fernandez-Cruz, K.L.J Ott, Multi-locus interactions predict risk for post-ptca restenosis: an approach to the genetic analysis of common complex disease, Pharmacogcnomics J. 2 (2002) 197-201.