Ponencia
Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization
Autor/es | Harari, Óscar
Rubio Escudero, Cristina Zwir, Igor |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2007 |
Fecha de depósito | 2022-12-01 |
Publicado en |
|
ISBN/ISSN | 978-3-540-71782-9 0302-9743 |
Resumen | A critical challenge of the postgenomic era is to understand how
genes are differentially regulated in and between genetic networks. The fact
that such co-regulated genes may be differentially regulated suggests that ... A critical challenge of the postgenomic era is to understand how genes are differentially regulated in and between genetic networks. The fact that such co-regulated genes may be differentially regulated suggests that subtle differences in the shared cis-acting regulatory elements are likely significant, however it is unknown which of these features increase or reduce expression of genes. In principle, this expression can be measured by microarray experi ments, though they incorporate systematic errors, and moreover produce a lim ited classification (e.g. up/down regulated genes). In this work, we present an unsupervised machine learning method to tackle the complexities governing gene expression, which considers gene expression data as one feature among many. It analyzes features concurrently, recognizes dynamic relations and gen erates profiles, which are groups of promoters sharing common features. The method makes use of multiobjective techniques to evaluate the performance of profiles, and has a multimodal approach to produce alternative descriptions of same expression target. We apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Es cherichia coli and Salmonella enterica. Our analysis uncovered profiles that were experimentally validated, suggesting correlations between promoter regu latory features and gene expression kinetics measured by green fluorescent pro tein (GFP) assays. |
Agencias financiadoras | Ministerio de Ciencia Y Tecnología (MCYT). España |
Identificador del proyecto | BIO2004-0270-E |
Cita | Harari, Ó., Rubio Escudero, C. y Zwir, I. (2007). Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization. En EvoBIO 2007: 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (68-77), Valencia, España: Springer. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
Targeting differentially co-re ... | 467.5Kb | [PDF] | Ver/ | |