Multiple threshold spatially uniform reliefF for the genetic analysis of complex human diseases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7833 LNCS, Issue , 2013, Pages 1-10

  Granizo Mackenzie, Delaney ^a   Moore, Jason H ^a  

^a DARTMOUTH COLLEGE   (United States)

Author keywords

Epistasis; Genetics; Heuristic; Interaction; Real Data; Relief; Weighting

Indexed keywords

EPISTASIS; GENETICS; HEURISTIC; INTERACTION; REAL DATA; RELIEF; WEIGHTING;

ALGORITHMS; DATA MINING; GENE EXPRESSION; LEARNING SYSTEMS;

BIOINFORMATICS;

EID: 84875117583     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37189-9_1     Document Type: Conference Paper

References (11)

1. Cordell, J.H.: Detecting genegene interactions that underlie human diseases. Nature Reviews Genetics 489, 392-404 (2009)
2. Greene, C.S., Himmelstein, D.S., Kiralis, J., Moore, J.H.: The Informative Extremes: Using Both Nearest and Farthest Individuals Can Improve Relief Algorithms in the Domain of Human Genetics. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 182-193. Springer, Heidelberg (2010)
3. Greene, C.S., Penrod, N.M., Kiralis, J., Moore, J.H.: Spatially uniform relieff (surf) for computationally-efficient filtering of gene-gene interactions. BioData Mining 2 (2009)
4. Kira, K., Rendell, L.A.: A practical approach to feature selection. Machine Learning, 249-256 (1992)
5. Kononenko, I.: Estimating Attributes: Analysis and Extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171-182. Springer, Heidelberg (1994)
6. Moore, J.H.: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data (2007)
7. Moore, J.H., Ritchie, M.D.: The challenges of whole-genome approaches to common diseases. JAMA 291, 1642-1643 (2004)
8. Moore, J.H., Williams, S.M.: Epistasis and its implications for personal genetics. AJHG 85, 309-320 (2009)
9. Sokal, R.R., Rohlf, F.J.: Biometry: the principles and practice of statistics in biological research, 3rd edn.
10. Thomas, D.: Gene-environment-wide association studies. Nat. Rev. Genetics 11, 259-272 (2010)
11. Velez, D.R., White, B.C., Motsinger, A.A., Bush, W.S., Ritchie, M.D., Williams, S.M., Moore, J.H.: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genetic Epidemiology 31, 306-315 (2007)