Ponencia
Discovering α–patterns from gene expression data
Autor/es | Rodríguez Baena, Domingo S.
Díaz Díaz, Norberto Aguilar Ruiz, Jesús Salvador Nepomuceno Chamorro, Isabel de los Ángeles |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2007 |
Fecha de depósito | 2022-07-21 |
ISBN/ISSN | 978-3-540-77225-5 0302-9743 |
Resumen | The biclustering techniques have the purpose of finding subsets of
genes that show similar activity patterns under a subset of conditions. In this
paper we characterize a specific type of pattern, that we have called ... The biclustering techniques have the purpose of finding subsets of genes that show similar activity patterns under a subset of conditions. In this paper we characterize a specific type of pattern, that we have called α–pattern, and present an approach that consists in a new biclustering algorithm specifically designed to find α–patterns, in which the gene expression values evolve across the experimental conditions showing a similar behavior inside a band that ranges from 0 up to a pre–defined threshold called α. The α value guarantees the co– expression among genes. We have tested our method on the Yeast dataset and compared the results to the biclustering algorithms of Cheng & Church (2000) and Aguilar & Divina (2005). Results show that the algorithm finds interesting biclusters, grouping genes with similar behaviors and maintaining a very low mean squared residue. |
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