Efficient statistical significance approximation for local similarity analysis of high-throughput time series data | Zendy

Xiaoling Zhang | Zendy; Dongmei Ai | Zendy; Jacob A. Cram | Zendy; Jed A. Fuhrman | Zendy; Fengzhu Sun | Zendy

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Efficient statistical significance approximation for local similarity analysis of high-throughput time series data

Author(s) -

Xiaoling Zhang,

Dongmei Ai,

Jacob A. Cram,

Jed A. Fuhrman,

Fengzhu Sun

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts668

Subject(s) - time series , series (stratigraphy) , computer science , similarity (geometry) , statistical analysis , data mining , throughput , statistics , algorithm , mathematics , artificial intelligence , machine learning , biology , telecommunications , paleontology , image (mathematics) , wireless

Local similarity analysis of biological time series data helps elucidate the varying dynamics of biological systems. However, its applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation.

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