Aggregation of Similarity Measures for Ortholog Detection: Validation with Measures Based on Rough Set Theory

Authors

  • Reinier Millo Sánchez Universidad Central "Marta Abreu" de Las Villas
  • Deborah Galpert Cañizares
  • Gladys Casa Cardoso
  • Ricardo Grau Ábalo
  • Leticia Arco García
  • María Matilde García Lorenzo
  • Miguel Ángel Fernández Marin

DOI:

https://doi.org/10.13053/cys-18-1-1580

Keywords:

Similarity measures, ortholog genes, mcl clustering, ortholog assignment, rough set theory, class imbalance.

Abstract

This paper presents a novel algorithm for ortholog detection that involves the aggregation of similarity measures characterizing the relationship between gene pairs of two genomes. The measures are based on the alignment score, the length of the sequences, the membership in the conserved regions as well as on the protein physicochemical profile. The clustering step over the similarity bipartite graph is performed by using the Markov clustering algorithm (MCL). A new ortholog assignment policy is applied over the homology groups obtained in the graph clustering. The classification results are validated with the Saccharomyces Cerevisiae and the Schizosaccharomyces Pombe genomes with the ortholog list of the INPARANOID 7.0 algorithm with the Adjusted Rand Index (ARI) external measure. Other validation measures based on the rough set theory are applied to calculate the quality of the classification dealing with class imbalance.

Downloads

Published

2014-04-03

Issue

Vol. 18 No. 1 (2014): 18(1) January-March 2014

Section

Articles

License 

Hereby I transfer exclusively to the Journal "Computación y 
Sistemas", published by the Computing Research Center (CIC-IPN),
the Copyright of the aforementioned paper. I also accept that these 
rights will not be transferred to any other publication, in any other 
format, language or other existing means of developing.
I certify that the paper has not been previously disclosed or simultaneo
usly submitted to any other publication, and that it does not contain 
material whose publication would violate the Copyright or other 
proprietary rights of any person, company or institution. I certify that 
I have the permission from the institution or company where I work or 
study to publish this work.
The representative author accepts the responsibility for the publication
of this paper on behalf of each and every one of the authors. 
This transfer is subject to the following conditions:
  • The authors retain all ownership rights (such as patent rights) of this work, except for the publishing rights transferred to the CIC, through this document.
  • Authors retain the right to publish the work in whole or in part in any book they are the authors or publishers. They can also make use of this work in conferences, courses, personal web pages, and so on.
  • Authors may include working as part of his thesis, for non-profit distribution only.