Speed and accuracy improvement of higher-order epistasis detection on CUDA-enabled GPUs

[1]  Jorge González-Domínguez,et al.  Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs , 2016, Euro-Par Workshops.

[2]  Sabela Ramos,et al.  Parallel Pairwise Epistasis Detection on Heterogeneous Computing Architectures , 2016, IEEE Transactions on Parallel and Distributed Systems.

[3]  Bertil Schmidt,et al.  High-speed exhaustive 3-locus interaction epistasis analysis on FPGAs , 2015, J. Comput. Sci..

[4]  Bertil Schmidt,et al.  GPU-accelerated exhaustive search for third-order epistatic interactions in case-control studies , 2015, J. Comput. Sci..

[5]  Matthias Reumann,et al.  High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies , 2015, Health Inf. Sci. Syst..

[6]  B. Zupan,et al.  Heterogeneous computing architecture for fast detection of SNP-SNP interactions , 2014, BMC Bioinformatics.

[7]  Kyung-Ah Sohn,et al.  Fast detection of high-order epistatic interactions in genome-wide association studies using information theoretic measure , 2014, Comput. Biol. Chem..

[8]  Yi Pan,et al.  Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering , 2014, BMC Bioinformatics.

[9]  Karsten M. Borgwardt,et al.  EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units , 2011, European Journal of Human Genetics.

[10]  M. L. Calle,et al.  Model‐Based Multifactor Dimensionality Reduction for detecting epistasis in case–control data in the presence of noise , 2011, Annals of human genetics.

[11]  Jiang Gui,et al.  A Robust Multifactor Dimensionality Reduction Method for Detecting Gene–Gene Interactions with Application to the Genetic Analysis of Bladder Cancer Susceptibility , 2011, Annals of human genetics.

[12]  Lin He,et al.  SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder , 2010, Cell Research.

[13]  Qiang Yang,et al.  BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies , 2010, American journal of human genetics.

[14]  H. Cordell.  Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[15]  Yi Yu,et al.  Performance of random forest when SNPs are in linkage disequilibrium , 2009, BMC Bioinformatics.

[16]  Qiang Yang,et al.  SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies , 2009, Bioinform..

[17]  Andrea Richter,et al.  RET Gly691Ser mutation is associated with primary vesicoureteral reflux in the French‐Canadian population from Quebec , 2008, Human mutation.

[18]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[19]  Jun S. Liu,et al.  Bayesian inference of epistatic interactions in case-control studies , 2007, Nature Genetics.

[20]  Lester L. Peters,et al.  Genome-wide association study identifies novel breast cancer susceptibility loci , 2007, Nature.

[21]  M. Jarvelin,et al.  A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity , 2007, Science.

[22]  R. Culverhouse,et al.  The Use of the Restricted Partition Method with Case-Control Data , 2007, Human Heredity.

[23]  H. Cordell.  Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. , 2002, Human molecular genetics.

[24]  C. Sing,et al.  A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. , 2001, Genome research.

[25]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[26]  Bill P. Buckles,et al.  Algorithm 515: Generation of a Vector from the Lexicographical Index [G6] , 1977, TOMS.

[27]  Tao Jiang,et al.  Detecting genome-wide epistases based on the clustering of relatively frequent items , 2012, Bioinform..

[28]  Can Yang,et al.  Comments on 'An empirical comparison of several recent epistatic interaction detection methods' , 2012, Bioinform..

[29]  Qiang Yang,et al.  Predictive rule inference for epistatic interaction detection in genome-wide association studies , 2010, Bioinform..