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10.1186/s12863-015-0314-9

http://scihub22266oqcxt.onion/10.1186/s12863-015-0314-9
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C4895284!4895284!26867108
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suck abstract from ncbi

pmid26867108      BMC+Genet 2016 ; 17 (Suppl 2): ä
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  • Pathway-based analyses #MMPMID26867108
  • Kent JW
  • BMC Genet 2016[]; 17 (Suppl 2): ä PMID26867108show ga
  • Background: New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation andpenalties for multiple testing. Methods: The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Results: Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. Conclusions: The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.
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