DSpace
 
  Repositório Institucional da Universidade de Aveiro > Departamento de Matemática > MAT - Artigos >
 Improving the performance of the iterative signature algorithm for the identification of relevant patterns
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/6913

title: Improving the performance of the iterative signature algorithm for the identification of relevant patterns
authors: Freitas, A.
Afreixo, V.
Pinheiro, M.
Oliveira, J.L.
Moura, G.
Santos, M.
keywords: Biclustering
Codon
Iterative signature algorithm
Median
Microarray
issue date: 2011
publisher: Wiley
abstract: The iterative signature algorithm (ISA) has become very attractive to detect co-regulated genes from microarray data matrices and can be a useful tool for the identification of similar patterns in many other kinds of numerical data matrices. Nevertheless, its algorithmic strategy exhibits some limitations since it is based on statistical behavior of the average and considers averages weighted by scores not necessarily positive. Hence, we propose to take the median instead of the average and to use absolutes scores in ISA's structure. Furthermore, a generalized function is also introduced in the algorithm in order to improve its algorithmic strategy for detecting high value or low value biclusters. The effects of these simple modifications on the performance of the biclustering algorithm are evaluated through an experimental comparative study involving synthetic data sets and real data from the organism Saccharomyces cerevisiae. The experimental results show that the proposed variations of ISA outperform the original version in many situations. Absolute scores in ISA are shown to be essential for the correct interpretation of the biclusters found by the algorithm. The median instead of the average turns the biclustering algorithm more resilient to outliers in the data sets. Copyright © 2011 Wiley Periodicals, Inc.
URI: http://hdl.handle.net/10773/6913
ISSN: 1932-1864
publisher version/DOI: dx.doi.org/10.1002/sam.10104
source: Statistical Analysis and Data Mining
appears in collectionsMAT - Artigos

files in this item

file description sizeformat
FreitasEtAl2011.pdf888.92 kBAdobe PDFview/open
statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! RCAAP OpenAIRE DeGóis
ria-repositorio@ua.pt - Copyright ©   Universidade de Aveiro - RIA Statistics - Powered by MIT's DSpace software, Version 1.6.2