Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: The CHAMBER algorithm

PLoS ONE

Volumn 4, Issue 3, 2009, Pages

  Mushlin, Richard A ^a   Gallanger, Stephen ^b   Kershenbaum, Aaron ^c   Rebbeck, Timothy R ^b  

^a PSYCHOGENICS INC   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CATECHOL ESTROGEN; CYTOCHROME P450 1A1; CYTOCHROME P450 1A2; ESTROGEN; GLUCURONOSYLTRANSFERASE 1A1; SULFOTRANSFERASE 1A1; BIOLOGICAL MARKER;

AFRICAN AMERICAN; ARTICLE; BREAST CANCER; CANCER RISK; CAUCASIAN; CLIQUE FINDING FOR HETEROGENEITY AND MULTIDIMENSIONALITY IN BIOMARKER EPIDEMIOLOGY RESEARCH; COMPUTER SIMULATION; CONTROLLED STUDY; ENDOMETRIUM CANCER; ESTROGEN METABOLISM; GENETIC ALGORITHM; GENETIC ASSOCIATION; GENETIC HETEROGENEITY; GENETIC RISK; GENETIC VARIABILITY; GENOTYPE; HORMONE METABOLISM; HUMAN; MAJOR CLINICAL STUDY; POPULATION BASED CASE CONTROL STUDY; ALGORITHM; BREAST TUMOR; CASE CONTROL STUDY; EPIDEMIOLOGY; FEMALE; GENETICS; OVARY TUMOR; POPULATION GENETICS;

ALGORITHMS; BIOLOGICAL MARKERS; BREAST NEOPLASMS; CASE-CONTROL STUDIES; EPIDEMIOLOGIC STUDIES; FEMALE; GENETICS, POPULATION; HUMANS; OVARIAN NEOPLASMS;

EID: 62849094154     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0004862     Document Type: Article

References (41)

1. Lander E (1996) The new genomics: global views of biology. Science 274: 536-539.
2. Breiman L, Friedman R, Olshen R, Stone C (1984) Classification and Regression Trees. Boca Raton: Chapman and Hall.
3. Foulkes AS, Reilly M, Zhou L, Wolfe M, Rader DJ (2005) Mixed modelling to characterize genotype-phenotype associations. Stat Med 24: 775-789.
4. Huang J, Lin A, Narasimhan B, Quertermous T, Hsiung CA, et al. (2004) Tree-structured supervised learning and the genetics of hypertension. Proc Natl Acad Sci U S A 101: 10529-10534.
5. Pavlov Y (1997) Random Forests. Probability Methods in Discrete Mathematics 1.
6. Nelson MR, Kardia SL, Ferrell RE, Sing CF (2001) A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Research 11: 458-470.
7. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, et al. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. American Journal of Human Genetics 69: 138-147.
8. Hoh J, Ott J (2000) Scan statistics to scan markers for susceptibility genes. Proceedings of the National Academy of Sciences of the United States of America 97: 9615-9617.
9. Friedman J (1991) Multivariate Adaptive Regression Splines. Annals of Statistics 19: 1-66.
10. Schapire R (1990) The Strength of Weak Learnability. Machine Learning 5: 197-227.
11. Vapnik V, Chapelle O (2000) Bounds on error expectation for support vector machines. Neural Computation 12: 2013-2036.
12. Friedman J, Tukey J (1974) A project pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, Series C 23: 881-889.
13. Tahri-Daizadeh N, Tregouet DA, Nicaud V, Manuel N, Cambien F, et al. (2003) Automated detection of informative combined effects in genetic association studies of complex traits. Genome Research 13: 1952-1960.
14. Tahri-Daizadeh N, Tregouet DA, Nicaud V, Poirier O, Cambien F, et al. (2004) Exploration of multilocus effects in a highly polymorphic gene, the apolipoprotein (APOB) gene, in relation to plasma apoB levels. Annals of Human Genetics 68: 405-418.
15. Conti DV, Cortessis V, Molitor J, Thomas DC (2003) Bayesian modeling of complex metabolic pathways. Human Heredity 56: 83-93.
16. Cortessis V, Thomas DC (2004) Toxicokinetic genetics: an approach to gene-environment and gene-gene interactions in complex metabolic pathways. IARC Scientific Publications. pp 127-150.
17. Moore J, White B (2007) Tuning ReliefF for genome-wide genetic analysis. Lecture Notes in Computer Science 4447: 166-175.
18. Kira K, Rendell L (1992) A practical approach to feature selection. In: Sleeman D, Edwards P, eds. Proceedings of the Ninth International Workshop on Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers. pp 249-256.
19. Kononenko I. Estimating attributes: analysis and extension of Relief. Proceedings of the European Conference on Machine Learning. New York, NY: Springer, 171-182.
20. Chen J, Yu K, Hsing A, Therneau TM (2007) A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genet Epidemiol 31: 238-251.
21. Lepre J, Rice J, Tu Y, Stolovitzky GA (2004) GenesΩork: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data. Bioinformatics 20: 1033-1044.
22. Ruczinski I, Kooperberg C, LeBlanc M (2003) Logic Regression. Journal of Computational and Graphical Statistics 12: 475-511.
23. Park MY, Hastie T (2008) Penalized logistic regression for detecting gene interactions. Biostatistics 9: 30-50.
24. Brinza D, Zelikovsky A (2006) 2SNP: scalable phasing based on 2-SNP haplotypes. Bioinformatics 22: 371-373.
25. Michie D, Spiegelhalter D, Taylor C (1994) Machine Learning, Neural and Statistical Classification: Ellis Horwood.
26. Schork NJ, Fallin D, Thiel B, Xu X, Broeckel U, et al. (2001) The future of genetic case-control studies. Adv Genet 42: 191-212.
27. Shannon WD, Province MA, Rao DC (2001) Tree-based recursive partitioning methods for subdividing sibpairs into relatively more homogeneous subgroups. Genet Epidemiol 20: 293-306.
28. Hauser ER, Watanabe RM, Duren WL, Bass MP, Langefeld CD, et al. (2004) Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 27: 53-63.
29. Thornton-Wells TA, Moore JH, Haines JL (2004) Genetics, statistics and human disease: analytical retooling for complexity. Trends Genet 20: 640-647.
30. Rebbeck TR, Troxel AB, Walker AH, Panossian S, Gallagher S, et al. (2007) Pairwise combinations of estrogen metabolism genotypes in postmenopausal breast cancer etiology. Cancer Epidemiol Biomarkers Prev 16: 444-450.
31. Rebbeck TR, Troxel AB, Wang Y, Walker AH, Panossian S, et al. (2006) Estrogen sulfation genes, hormone replacement therapy, and endometrial cancer risk. Journal of the National Cancer Institute 98: 1311-1320.
32. Rebbeck TR, Troxel AB, Shatalova EG, Blanchard R, Norman S, et al. (2007) Lack of effect modification between estrogen metabolism genotypes and combined hormone replacement therapy in postmenopausal breast cancer risk. Cancer Epidemiol Biomarkers Prev 16: 1318-1320.
33. Erlenkotter D (1978) A Dual-Based Procedure for Uncapacitated Facility Location. Operations Research 26: 992-1009.
34. Witten I, Frank E (2005) Data Mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann.
35. Liehr JG (1990) Genotoxic effects of estrogens. Mutat Res 238: 269-276.
36. Mushlin R, Kershenbaum A, Gallagher S, Rebbeck T (2007) A graph-theoretical approach for pattern discovery in epidemiological research. IBM Systems Journal In Press.
37. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B 57: 289-300.
38. Thomas H, Cormen C, Leiserson R, Stein C (2001) Introduction to Algorithms: Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein (2001). "26", Introduction to Algorithms, second edition MIT Press and McGraw-Hill. 643-700. ISBN 0-262-53196-8. 643-700 p.
39. Jaccard P (1908) Nouvelles recherches sur la distribution florale. Société Vaudoisedes Sciences Naturelles 44: 223-270.
40. Strom B, Schinnar R, Weber AL, Bunin G, Berlin JA, Baumgarten M, DeMichele A, Rubin SC, Berlin M, Troxel AB, Rebbeck TR (2006) Case-control study of postmenopausal hormone replacement therapy and endometrial cancer. American Journal of Epidemiology 164: 775-786.
41. Quinlan JR (1993) C4.5 : Programs for machine learning. San Francisco: Morgan Kaufmann.