CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs. Summary: Cluster and set-cover algorithms are developed to obtain a set of tag single nucleotide polymorphisms (SNPs) that can represent all the known SNPs in a chromosomal region, subject to the constraint that all SNPs must have a squared correlation R2 > C with at least one tag SNP, where C is specified by the user. Availability:

References in zbMATH (referenced in 7 articles )

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  1. Panigrahi, Snigdha; Taylor, Jonathan: Scalable methods for Bayesian selective inference (2018)
  2. Cilibrasi, Rudi L.; Vitányi, Paul M. B.: A fast quartet tree heuristic for hierarchical clustering (2011)
  3. Liang, Yulan; Kelemen, Arpad: Sequential support vector regression with embedded entropy for SNP selection and disease classification (2011)
  4. Nascimento, Mariá C. V.; Toledo, Franklina M. B.; de Carvalho, André C. P. L. F.: Investigation of a new GRASP-based clustering algorithm applied to biological data (2010)
  5. Field, Helen I.; Scollen, Serena A.; Luccarini, Craig; Baynes, Caroline; Morrison, Jonathan; Dunning, Alison M.; Easton, Douglas F.; Pharoah, Paul D. P.: Seq4snps: new software for retrieval of multiple, accurately annotated DNA sequences, ready formatted for SNP assay design (2009) ioport
  6. Guo, Mao-Zu; Wang, Jun; Wang, Chun-yu; Liu, Yang: A hybrid clustering and graph based algorithm for tagSNP selection (2009) ioport
  7. Liang, Yulan; Kelemen, Arpad: Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases (2008)