Gene expression modeling through positive boolean functions
International Journal of Approximate Reasoning
Identification of Co-regulated Signature Genes in Pancreas Cancer- A Data Mining Approach
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Research article: An elastic network model to identify characteristic stress response genes
Computational Biology and Chemistry
International Journal of Data Mining and Bioinformatics
Experiment specific expression patterns
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Integrating heterogeneous microarray data sources using correlation signatures
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
Biological specifications for a synthetic gene expression data generation model
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Hi-index | 3.84 |
Motivation: Standard analysis routines for microarray data aim at differentially expressed genes. In this paper, we address the complementary problem of detecting sets of differentially co-expressed genes in two phenotypically distinct sets of expression profiles. Results: We introduce a score for differential co-expression and suggest a computationally efficient algorithm for finding high scoring sets of genes. The use of our novel method is demonstrated in the context of simulations and on real expression data from a clinical study.