Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan

4 Issues per year


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
See all formats and pricing
More options …
Volume 9, Issue 3

Issues

Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

D. Calvo-Dmgz
  • ESEI: Escuela Superior de Enxeñería Informática, University of Vigo, Ed. Politécnico, Campus Universitario As Lagoas s/n 32004 Ourense, http://www.esei.uvigo.es, Spain
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ J. F. Gálvez
  • Corresponding author
  • ESEI: Escuela Superior de Enxeñería Informática, University of Vigo, Ed. Politécnico, Campus Universitario As Lagoas s/n 32004 Ourense, http://www.esei.uvigo.es, Spain
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ D. Glez-Peña
  • ESEI: Escuela Superior de Enxeñería Informática, University of Vigo, Ed. Politécnico, Campus Universitario As Lagoas s/n 32004 Ourense, http://www.esei.uvigo.es, Spain
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ S. Gómez-Meire
  • ESEI: Escuela Superior de Enxeñería Informática, University of Vigo, Ed. Politécnico, Campus Universitario As Lagoas s/n 32004 Ourense, http://www.esei.uvigo.es, Spain
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ F. Fdez-Riverola
  • ESEI: Escuela Superior de Enxeñería Informática, University of Vigo, Ed. Politécnico, Campus Universitario As Lagoas s/n 32004 Ourense, http://www.esei.uvigo.es, Spain
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2012-199

Summary

DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

About the article

Published Online: 2016-10-18

Published in Print: 2012-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 9, Issue 3, Pages 1–17, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2012-199.

Export Citation

© 2012 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Comments (0)

Please log in or register to comment.
Log in