<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites. We also show how to use the algorithm to find a human readable model of the IRE (Iron-Responsive Element) and to find IRE sequences. The method, although universal, yields results which are of quality comparable to those obtained by reference methods. In contrast to reference methods, this approach uses models that operate on sequence patterns, which facilitates interpretation of the results by humans.</p></abstract>

This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites. We also show how to use the algorithm to find a human readable model of the IRE (Iron-Responsive Element) and to find IRE sequences. The method, although universal, yields results which are of quality comparable to those obtained by reference methods. In contrast to reference methods, this approach uses models that operate on sequence patterns, which facilitates interpretation of the results by humans.

KIS: An automated attribute induction method for classification of DNA sequences

Institute of Electronic Systems Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"KIS: An automated attribute induction method for classification of DNA sequences"}

This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that...

KIS: An automated attribute induction method for classification of DNA sequences

Online veröffentlicht: 28. Sept. 2012

Seitenbereich: 711 - 721

DOI: https://doi.org/10.2478/v10006-012-0053-2

Schlüsselwörter
classification, optimization, annotation, patterns

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

KIS: An automated attribute induction method for classification of DNA sequences

Online veröffentlicht: 28. Sept. 2012

Seitenbereich: 711 - 721

DOI: https://doi.org/10.2478/v10006-012-0053-2

Schlüsselwörterclassification, optimization, annotation, patterns

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
classification, optimization, annotation, patterns