MOTIVATION: The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. RESULTS: We propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network. We demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures. AVAILABILITY: ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info).

UNLABELLED: We describe here an exploratory tool, called FunNet, which implements an original systems biology approach, aiming to improve the biological relevance of the modular interaction patterns identified in transcriptional co-expression networks. A suitable analytical model, involving two abstraction layers, has been devised to relate expression profiles to the knowledge on transcripts' biological roles, extracted from genomic databases, into a comprehensive exploratory framework. This approach has been implemented into a user-friendly web tool to promote its open use by the community. AVAILABILITY: http://www.funnet.info

MOTIVATION: Microarray experiments that allow simultaneous expression profiling of thousands of genes in various conditions (tissues, cells or time) generate data whose analysis raises difficult problems. In particular, there is a vast disproportion between the number of attributes (tens of thousands) and the number of examples (several tens). Dimension reduction is therefore a key step before applying classification approaches. Many methods have been proposed to this purpose, but only a few of them considered a direct quantification of transcriptional interactions. We describe and experimentally validate a new dimension reduction and feature construction method, which assesses interactions between expression profiles to improve microarray-based classification accuracy. RESULTS: Our approach relies on a mutual information measure that exposes some elementary constituents of the information contained in a pair of gene expression profiles. We show that their analysis implies a term that represents the information of the interaction between the two genes. The principle of our method, called FeatKNN, is to exploit the information provided by highly synergic gene pairs to improve classification accuracy. First, a heuristic search selects the most informative gene pairs. Then, for each selected pair, a new feature, representing the classification margin of a KNN classifier in the gene pairs space, is constructed. We show experimentally that the interactional information has a degree of significance comparable to that of the gene expression profiles considered separately. Our method has been tested with different classifiers and yielded significant improvements in accuracy on several public microarray databases. Moreover, a synthetic assessment of the biological significance of the concept of synergic gene pairs suggested its ability to uncover relevant mechanisms underlying interactions among various cellular processes.