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This page is a summary of: Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences, PLoS Computational Biology, June 2009, PLOS,
DOI: 10.1371/journal.pcbi.1000421.
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SciVee Video (7:47 min.)
A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarc
MACML Software
link to download software for clustering sequences into heterogeneous regions with specific site types, without requiring any prior knowledge.
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