A Transcriptional Approach to Gene Clustering

— We present an integrative method for clustering coregulated genes and elucidating their underlying regulatory mechanisms. We use multi-state partition functions and thermodynamic models to derive six distinct correlation classes that correspond to various Protein-Protein and Protein-DNA interactions. We then introduce a biclustering algorithm for clustering genes based on the correlations exhibited in their expression profiles. We evaluate the functional enrichment and statistical significance of the resulting clusters using precisionrecall curves. Our results show that classification performance can be optimized by selecting the corresponding correlation class. Additionally, there is a significant improvement over single class biclustering when we use multi-class support vector machines and biclustering scores as features. Furthermore, the analysis of the upstream regions of all genes comprising each cluster shows that the derived correlation classes capture the expression of ...

• Type: Conference
• Year: 2005
• Where: CIBCB
• Authors: Ilias Tagkopoulos
• Keywords: CIBCB 2005, Computational Biology, Conserved Sequences, Correlation Classes, Distinct Correlation Classes