Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection

Autor: Urbanowicz Ryan J, Kiralis Jeff, Fisher Jonathan M, Moore Jason H

Publikováno v: BioData Mining, Vol 5, Iss 1, p 15 (2012)

Abstract Background Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of lo

Externí odkaz: https://doaj.org/article/e2bfeff8932340e28ecd2bab92c9d349

Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions

Autor: Greene Casey S, Penrod Nadia M, Kiralis Jeff, Moore Jason H

Publikováno v: BioData Mining, Vol 2, Iss 1, p 5 (2009)

Abstract Background Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single

Externí odkaz: https://doaj.org/article/03ef5aa11e7e425a934cd765fe5b3fbe

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

Autor: Hu, Ting, Chen, Yuanzhu, Kiralis, Jeff W, Collins, Ryan L, Wejse, Christian, Sirugo, Giorgio, Williams, Scott M, Moore, Jason H

Publikováno v: Hu, T, Chen, Y, Kiralis, J W, Collins, R L, Wejse, C, Sirugo, G, Williams, S M & Moore, J H 2013, ' An information-gain approach to detecting three-way epistatic interactions in genetic association studies ', Journal of the American Medical Informatics Association, vol. 20, no. 4 . https://doi.org/10.1136/amiajnl-2012-001525

Background Epistasis has been historically used todescribe the phenomenon that the effect of a givengene on a phenotype can be dependent on one or moreother genes, and is an essential element forunderstanding the association between genetic andphenot

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pure_au_____::07d1fddfa1c031452769caf1fc069aaa
https://pure.au.dk/portal/da/publications/an-informationgain-approach-to-detecting-threeway-epistatic-interactions-in-genetic-association-studies(777f5ec8-dea8-4abc-9c0b-3f22ae0afd80).html

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Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction.

Autor: Fisher, Jonathan M., Andrews, Peter, Kiralis, Jeff, Sinnott-Armstrong, Nicholas A., Moore, Jason H.

Publikováno v: Evolutionary Computation, Machine Learning & Data Mining in Bioinformatics (9783642371882); 2013, p200-211, 12p