Tomas Arredondo

Information Theoretics Based Sequence Pattern Discriminant Algorithms

Applications in Bioinformatic Data Mining

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 264
• Erscheinungstermin: 7. Januar 2010
• Englisch
• Abmessung: 220mm x 150mm x 16mm
• Gewicht: 357g
• ISBN-13: 9783838337104
• ISBN-10: 3838337107
• Artikelnr.: 28418356

Autorenporträt

Tomas V. Arredondo has a PhD in EE from FAU. He is an Assistant Professor in Electronic Engineering at UTFSM in Valparaiso,Chile. Prior to that he was as a Distinguished Member of Technical Staff in Siemens specializing in software and database development. His areas of interest include software design, datamining, bioinformatics and softcomputing.