Genome Clustering

From Linguistic Models to Classification of Genetic Texts
1. Auflage. 2010
ISBN: 978-3-642-12951-3
Verlag: Springer

Buch, Englisch, Band 286, 206 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 574 g

Reihe: Studies in Computational Intelligence

Knighting in sequence biology Edward N. Trifonov Genome classification, construction of phylogenetic trees, became today a major approach in studying evolutionary relatedness of various species in their vast - versity. Although the modern genome clustering delivers the trees which are very similar to those generated by classical means, and basic terminology is the same, the phenotypic traits and habitats are not anymore the playground for the classi- cation. The sequence space is the playground now. The phenotypic traits are - placed by sequence characteristics, “words”, in particular. Matter-of-factually, the phenotype and genotype merged, to confusion of both classical and modern p- logeneticists. Accordingly, a completely new vocabulary of stringology, information theory and applied mathematics took over. And a new brand of scientists emerged – those who do know the math and, simultaneously, (do?) know biology. The book is written by the authors of this new brand. There is no way to test their literacy in biology, as no biologist by training would even try to enter into the elite circle of those who masters their almost occult language. But the army of - formaticians, formal linguists, mathematicians humbly (or aggressively) longing to join modern biology, got an excellent introduction to the field of genome cl- tering, written by the team of their kin.

Zielgruppe

Research

Autoren/Hrsg.

Bolshoy, Alexander

Barzily, Zeev

Volkovich, Zeev

Kirzhner, Valery

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Biological Background.- Biological Classification.- Mathematical Models for the Analysis of Natural-Language Documents.- DNA Texts.- N-Gram Spectra of the DNA Text.- Application of Compositional Spectra to DNA Sequences.- Marker-Function Profile-Based Clustering.- Genome as a Bag of Genes – The Whole-Genome Phylogenetics.