ATHENA: The analysis tool for heritable and environmental network associations

Bioinformatics

Volumn 30, Issue 5, 2014, Pages 698-705

  Holzinger, Emily R ^a   Dudek, Scott M ^b   Frase, Alex T ^b   Pendergrass, Sarah A ^b   Ritchie, Marylyn D ^b  

^a NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER PROGRAM; GENETIC ASSOCIATION; GENOTYPE ENVIRONMENT INTERACTION; HUMAN; PHENOTYPE; SINGLE NUCLEOTIDE POLYMORPHISM;

GENE-ENVIRONMENT INTERACTION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 84897711239     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt572     Document Type: Article

References (31)

1. Aulchenko,Y.S. et al. (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics, 23, 1294-1296.
2. Bishop,C.M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, London.
3. Breiman,L. (2001) Random Forests. Machine Learning, 45, 5-32.
4. Bush,W.S. et al. (2009) Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac. Symp. Biocomput., 368-379.
5. Carniak,E. (1991) Bayesian networks without tears. AI Magazine, 50-63.
6. Chalise,P. and Fridley,B.L. (2012) Comparison of penalty functions for sparse canonical correlation analysis. Comput. Stat. Data Anal., 56, 245-254.
7. Cordell,H.J. (2009) Genome-wide association studies: detecting gene-gene interactions that underlie human diseases. Nat. Rev. Genet., 10, 392-404.
8. Dudek,S.M. et al. (2006) Data simulation software for whole-genome association and other studies in human genetics. Pac. Symp. Biocomput., 11, 499-510.
9. Goeman,J.J. (2010) L1 penalized estimation in the Cox proportional hazards model. Biom. J., 52, 70-84.
10. Holzinger,E.R. and Ritchie,M.D. (2012) Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics, 13, 213-222.
11. Holzinger,E.R. et al. (2011) ATHENA optimization: the effect of initial parameter settings across different genetic models. In: Pizzuti,C. et al. (ed.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 48-58.
12. Holzinger,E.R. et al. (2012) Comparison of methods for meta-dimensional data analysis using in silico and biological data sets. In: Giacobini,M. et al. (ed.) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 134-143.
13. Holzinger,E.R. et al. (2010) Initialization parameter sweep in ATHENA: optimizing neural networks for detecting gene-gene interactions in the presence of small main effects. Genet. Evol. Comput. Conf., 12, 203-210.
14. Huang,R.S. et al. (2007) A genome-wide approach to identify genetic variants that contribute to etoposide-induced cytotoxicity. Proc. Natl Acad. Sci. USA, 104, 9758-9763.
15. Ideker,T. et al. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet., 2, 343-372.
16. Jiang,X. et al. (2010) Identifying genetic interactions in genome-wide data using Bayesian networks. Genet. Epidemiol., 34, 575-581.
17. Koza,J.R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
18. Meng,Y.A. et al. (2009) Performance of random forest when SNPs are in linkage disequilibrium. BMC Bioinformatics, 10, 78.
19. Moore,J.H. et al. (2007) Symbolic modeling of epistasis. Hum. Hered., 63, 120-133.
20. O'Neill,M. and Ryan,C. (2001) Grammatical evolution. IEEE Trans. Evol. Comput., 5, 349-358.
21. Pareek,C.S. et al. (2011) Sequencing technologies and genome sequencing. J. Appl. Genet., 52, 413-435.
22. Pendergrass,S.A. et al. (2010) Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis. BioData Min., 3, 10.
23. R Development Core Team. (2011) R: a language and environment for statistical computing. ISBN 3900051070. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org (22 October 2013, date last accessed).
24. Reif,D.M. et al. (2004) Integrated analysis of genetic, genomic and proteomic data. Expert Rev. Proteomics, 1, 67-75.
25. Ritchie,M.D. et al. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet., 69, 138-147.
26. Schwarz,D.F. et al. (2010) On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics, 26, 1752-1758.
27. Tibshirani,R. (1996) Regression shrinkage and selection via the Lasso. J. R Stat. Soc. B, 58, 267-288.
28. Turner,S.D. et al. (2010) Grammatical evolution of neural networks for discovering epistasis among quantitative trait Loci. Lect. Notes Comput. Sci., 6023, 86-97.
29. Visscher,P.M. et al. (2012) Five years of GWAS discovery. Am. J. Hum. Genet., 90, 7-24.
30. Watanabe,R.M. (2011) Statistical issues in gene association studies. In: DiStefano,J.K. (ed.) Disease Gene Identification. Humana Press, Totowa, NJ, pp. 17-36.
31. Watters,J.W. (2004) Genome-wide discovery of loci influencing chemotherapy cytotoxicity. Proc. Natl Acad. Sci. USA, 101, 11809-11814.