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pubmed-article:19261718pubmed:abstractTextThe study of complex biological relationships is aided by large and high-dimensional data sets whose analysis often involves dimension reduction to highlight representative or informative directions of variation. In principle, information theory provides a general framework for quantifying complex statistical relationships for dimension reduction. Unfortunately, direct estimation of high-dimensional information theoretic quantities, such as entropy and mutual information (MI), is often unreliable given the relatively small sample sizes available for biological problems. Here, we develop and evaluate a hierarchy of approximations for high-dimensional information theoretic statistics from associated low-order terms, which can be more reliably estimated from limited samples. Due to a relationship between this metric and the minimum spanning tree over a graph representation of the system, we refer to these approximations as MIST (Maximum Information Spanning Trees).lld:pubmed
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pubmed-article:19261718pubmed:monthMaylld:pubmed
pubmed-article:19261718pubmed:issn1367-4811lld:pubmed
pubmed-article:19261718pubmed:authorpubmed-author:TidorBruceBlld:pubmed
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pubmed-article:19261718pubmed:pagination1165-72lld:pubmed
pubmed-article:19261718pubmed:dateRevised2011-4-11lld:pubmed
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pubmed-article:19261718pubmed:year2009lld:pubmed
pubmed-article:19261718pubmed:articleTitleMIST: Maximum Information Spanning Trees for dimension reduction of biological data sets.lld:pubmed
pubmed-article:19261718pubmed:affiliationComputer Science and Artificial Intelligence Laboratory, Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.lld:pubmed
pubmed-article:19261718pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:19261718pubmed:publicationTypeResearch Support, Non-U.S. Gov'tlld:pubmed
pubmed-article:19261718pubmed:publicationTypeResearch Support, N.I.H., Extramurallld:pubmed