DIME: DIME (Differential Identification using Mixture Ensemble)
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
||Cenny Taslim, with contributions from Dustin Potter, Abbasali Khalili and Shili Lin.
||Cenny Taslim <taslim.2 at osu.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Please use the canonical form
to link to this page.