Abstract
We propose a modeling approach based on Probabilistic Boolean Networks for the inference of genetic regulatory networks from gene expression time-course data in different biological conditions i.e. making use of the information contained in sets of genes and the interaction between genes rather than single-gene analyses. This model is a collection of traditional Probabilistic Boolean Networks. We also present an approach which is based on constrained prediction and Coefficient of Determination (COD) for the identification of the model from gene expression data. The modeling approach is applied in the context of pathway biology to the analysis of gene interaction networks.
Original language | English |
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Title of host publication | 2006 IEEE International Workshop on Genomic Signal Processing and Statistics |
Place of Publication | NEW YORK |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 45-46 |
Number of pages | 2 |
ISBN (Print) | 978-1-4244-0384-4 |
Publication status | Published - 2006 |
Event | IEEE International Workshop on Genomic Signal Processing and Statistics - College St Duration: 28 May 2006 → 30 May 2006 |
Conference
Conference | IEEE International Workshop on Genomic Signal Processing and Statistics |
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City | College St |
Period | 28/05/06 → 30/05/06 |