Data mining

Genetic Epidemiology

Volumn 29, Issue SUPPL., 2005, Pages

  Cupples, L Adrienne ^a   Bailey, Julia ^b   Cartier, Kevin C ^c   Falk, Catherine T ^d   Liu, Kuang Yu ^e   Ye, Yuanqing ^f   Yu, Robert ^g   Zhang, Heping ^f   Zhao, Hongyu ^f  

^a BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c CASE WESTERN RESERVE UNIVERSITY   (United States)

^d NEW YORK BLOOD CENTER   (United States)

^e BRIGHAM AND WOMEN S HOSPITAL   (United States)

^f YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^g UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Association studies; Haplotype estimation; Machine learning; Neural networks; Trees

Indexed keywords

ALCOHOLISM; ANALYTICAL ERROR; CONFERENCE PAPER; GENE IDENTIFICATION; GENETIC ASSOCIATION; GENETIC LINKAGE; GENETIC TRAIT; HAPLOTYPE; HUMAN; INFORMATION PROCESSING; LEARNING; METHODOLOGY; MOTIVATION;

ALCOHOLISM; ARTIFICIAL INTELLIGENCE; CHROMOSOME MAPPING; CYTOGENETIC ANALYSIS; DECISION TREES; GENETIC MARKERS; HAPLOTYPES; HUMANS; NEURAL NETWORKS (COMPUTER);

EID: 30344484272     PISSN: 07410395     EISSN: None     Source Type: Journal    
DOI: 10.1002/gepi.20117     Document Type: Conference Paper

References (21)

1. 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.
2. Breiman L. 1998. Arcing classifiers. Ann Stat 26:801-849.
3. Burges C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2:121-167.
4. Cartier KC, Baechle D. 2005. An artificial neural network for estimating haplotype frequencies. BMC Genet [Suppl] 6:129.
5. Chen L, Liu N, Wang S, Oh C, Carriero NJ, Zhao H. 2005. Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites. BMC Genet [Suppl] 6:130.
6. Efron B. 2004. Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J Am Stat Assoc 99:96-104.
7. Falk CT. 2005. Diagnosis of alcoholism based on neural network analysis of phenotypic risk factors. BMC Genet [Suppl] 6:131.
8. Freund Y, Mason L. 1999. The alternating decision tree learning algorithm. In: Bratko I, Dzeroski S, editors. The Proceedings of the Sixteenth International Conference on Machine Learning. San Francisco: Morgan Kaufman Publishers, Inc. p 124-133.
9. Lange C, Laird NM. 2002. On a general class of conditional tests for family-based association studies in genetics: the asymptotic distribution, the conditional power, and optimality considerations. Genet Epidemiol 23:165-180.
10. Liu K-Y, Lin J, Zhou X, Wong STC. 2005. Boosting alternating decision trees: modeling of disease trait information. BMC Genet [Suppl] 6:132.
11. Lo SH, Zheng T. 2004. A demonstration and findings of a statistical approach through reanalysis of inflammatory bowel disease data. Proc Natl Acad Sci USA 101:10386-10391.
12. Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C. 2004. Predicting genetic regulatory response using classification. Bioinformarics [Suppl] 20:232-240.
13. Pao Y. 1988. Adaptive pattern recognition and neural networks. New York: Addison Wesley.
14. Quinlan J. 1996. Bagging, boosting, and C4.5. In: The Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI96, IAAI96. Cambridge, MA: MIT Press. p 725-730. [http://www.aaai.org].
15. Rabinowitz D, Laird N. 2000. A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 50:211-223.
16. Storey JD. 2002. A direct approach to false discovery rates. J R Stat Soc [B] 64:479-498.
17. Wei L-Y, Huang C-L, Chen C-H. 2005. Data mining of GAW14 simulated data using rough set theory and tree-based methods. BMC Genet [Suppl] 6:133.
18. Windemuth C, Hahn A, Strauch K, Baur MP, Wienker TF. 1999. Linkage analysis in alcohol dependence. Genet Epidemiol [Suppl] 17:403-407.
19. Yang Q, Cui J, Chazaro I, Cupples LA, Demissie S. 2005. Power and type I error rate of false discovery rate approaches in genome-wide association studies. BMC Genet [Suppl] 6:134.
20. Ye Y, Zhong X, Zhang H. 2005. A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking. BMC Genet [Suppl] 6:135.
21. Yu R, Shete S. 2005. Analysis of alcoholism data using support vector machines. BMC Genet [Suppl] 6:136.