Available Software

• SIMPLE - Sequential Imputation for MultiPoint Linkage Estimation
  Calculates linkage statistics, such as lod scores and NPL statistics by Sequential Imputation.
  
  
• START
  Finds starting points for MCMC analysis performed on large, complex pedigrees and polymorphic markers.
  
  
• MAXPROC
  Calculates maximum likelihood estimates of linkage parameters under heterogeneity and their standard errors using EM and SEM algorithms
  
  
• CSI
  Uses affected sib pairs data and relative risks to construct a confidence set of markers within a prespecified genetic distance from the disease locus.
  
  
• DNC-MIX
  Models the distribution of the gene expression profile of a test sample as a mixture, with each component characterizing the expression levels in a class, and assigns a class label to each test sample.
  
  
• tagSNPfinder
  Select a subset of tagging SNPs using a forward selection algorithm or a cross entropy Monte Carlo algorithm based on a criterion that is dependent on both haplotype diversity and linkage disequilibrium.
  
  
• Pathway
  Use methylation profiles and clinical variables to group tumor samples into clusters and then organize them into a tree to represent tumor progression pathways that conform to strict heritability.
  
  
• DE-SAGE
  Analyze SAGE library data using a Bayesian hierachical and mixture modeling approach and RJMCMC computational algorithms.
  
  
• miRComp
  A filtering step of putative microRNA targets through aggregating the predictions by several algorithms using two composite statistics - composite ranks and composite "p-values".
  
  
• MC-PDT
  Perform test of linkage disequilibrium in the presence of linkage using pedigree data based on Monte Carlo samples of complete data given observed data.
  
  
• TopKCEMC
  A rank aggregation tool for integrating data from multiple sources based on ranks.
  
  
• rGLM
  Generalized linear modeling with regularization for Case-control association studies; suitable for both common disease/common variants and common disease/rare variance scenarios.
  
  
• GNG
  Robust classification for both methylation and gene expression data.

Acknowledgment and Disclaimer: Several of the software packages are based upon work supported partly by the National Science Foundation under Grant No. 9971770 and No. 0306800. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Last modified: Wednesday, December 31, 2008 12:10 PM.