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Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences
[dataset]
2009
SciVee
unpublished
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
doi:10.4016/12250.01
fatcat:kvn62dapebc73op3rh7z23qqt4