Introduction Knowledge discovery in bioinformatics

We’d like to welcome you to this special issue of the Intelligent Data Analysis journal devoted to Knowledge Discovery in Bioinformatics. The articles selected for this issue partially come from the special session with the same name, Knowledge Discovery in Bioinformatics, in BGRS 2008. Additional articles, on the same topic, have been included from regular contributions received by the IDA journal. The special session on Knowledge Discovery in Bioinformatics was the third of this kind of successful sessions hosted by three different conferences worldwide, the European Conference in Artificial Intelligence – ECAI 2004 (Valencia, Spain), the European Conference of Machine Learning – ECML/PKDD 2007 (Warsaw, Poland) and, finally, the Sixth International Conference on Bioinformatics of Genome Regulation and Structure – BGRS 2008 (Novosibirsk, Russia). The aim of these sessions has always been to show the interdisciplinary research carried out in the joint collaboration of data analysis/modeling and system biology, genomics or proteomics. The process of selecting the papers among the contributions submitted for the BGRS special session was a tough task. There was a great quality in the works presented and a broad variety in terms of both techniques and applications. Finally, those best representing the on-going research shown in the session were asked for an extended version including new results and a more detailed description of the techniques put in practices. The author had the opportunity to extend their work for few more months before a peer-reviewed process for the selection of the first four articles included in this selection: The article PROMETHEUS: Technology for Rapid Development of Biological Databases by Timonov and Miginsky presents a new technology for the development of biological databases, specifically designed for researchers and practitioners in different life sciences domains. Beslon et al., in their article entitled From Digital Genetics to Knowledge Discovery: Perspectives in Genetic Network Understanding, present an integrated approach to model regulatory networks in artificial organisms. The proposed model is compared by means of a data analysis process, similar to the one performed on living organisms (effect of gene knock-out in metabolic networks), including discussion about the application of artificial life methods as a benchmark for the study of biological networks. In the article Independent Component Analysis for Microarray Data Analysis by Malutan, G ómezVilda and Borda, the authors present a new methodology for the detection process in microarrays by multichannel differential expression. This new methodology, based on genomic signal processing, is able to identify unexpected hybridization dynamics. The article CliDaPa: A New Approach to Combining Clinical Data with DNA Microarrays by González et al. introduces a new algorithm to combine clinical data with different genomic/proteomic data. The