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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Evaluation of Descriptor Algorithms of Biological Sequences and Distance Measures for the Intelligent Cluster Index (ICIx)

verfasst von : Stefan Schildbach, Florian Heinke, Wolfgang Benn, Dirk Labudde

Erschienen in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

In hindsight of the previous decades, a rapid growth of data in all fields of life sciences is perceptible. Most notably is the general tendency of retaining well established techniques regarding specific biological requirements and common taxonomies for data classification. Therefore a change in perspective towards advanced technological concepts for persisting, organizing and analyzing these huge amounts of data is essential. The Intelligent Cluster Index (ICIx) is a modern technology capable of indexing multidimensional data through semantic criteria, qualified for this challenge. In this paper methodical approaches for indexing biological sequences with the ICIx are discussed and evaluated. This includes the examination of established methods concentrating on vector transformation as well as outlining the efficiency of different distance measures applied to these vectors. Based on our results, it becomes apparent that position conserving methods are superior to other approaches and that the applied distance measures heavily influence performance and quality.

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Vorheriges Kapitel eQuant - A Server for Fast Protein Model Quality Assessment by Integrating High-Dimensional Data and Machine Learning
Nächstes Kapitel A Holistic Approach to Testing Biomedical Hypotheses and Analysis of Biomedical Data
Fußnoten
1
Other commonly used notions for n-grams are k-, t- or n-tuples and k-, t- or n-mers.
 
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Metadaten
Titel
Evaluation of Descriptor Algorithms of Biological Sequences and Distance Measures for the Intelligent Cluster Index (ICIx)
verfasst von
Stefan Schildbach
Florian Heinke
Wolfgang Benn
Dirk Labudde
Copyright-Jahr
2016
Buch
Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-34099-9_33

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