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Information Theoretics Based Sequence Pattern Discriminant Algorithms


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This work refers to studies on information-theoretic (IT) aspects of data-sequence patterns and developing discriminant algorithms that enable distinguishing the features of underlying sequence patterns having characteristic, inherent stochastical attributes. Considered in this research are specific details on information-theoretics and entropy considerations vis-á-vis sequence patterns (having stochastical attributes) such as DNA sequences of molecular biology. Applying information-theoretic concepts (essentially in Shannon''s sense), the following distinct sets of metrics are developed and applied in the algorithms developed for data-sequence pattern-discrimination applications: (i) Divergence or cross-entropy algorithms of Kullback-Leibler type and of general Czizár class; (ii) statistical distance measures; (iii) ratio-metrics; (iv) Fisher type linear-discriminant measure; (v) complexity metric based on information redundancy; and a Fuzzy logic based measure. Relevant algorithms are used to test DNA sequences of human and some bacterial organisms.

Product Specifications
SKU :COC49664
AuthorTomas Arredondo
Number of Pages264
Publishing Year2010-01-07T00:00:00.000
Edition1 st
Book TypeGenetics (non-medical)
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-04-08 00:00:00
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