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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.
| Autorius: | Tomas Arredondo |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2010 |
| Knygos puslapių skaičius: | 264 |
| ISBN-10: | 3838337107 |
| ISBN-13: | 9783838337104 |
| Formatas: | 220 x 150 x 16 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Information Theoretics Based Sequence Pattern Discriminant Algorithms: Applications in Bioinformatic Data Mining“
