A discussion on the biological relevance of clustering results

The recent advances in genomic technologies and the availability of large-scale datasets call for the development of advanced data analysis techniques, such as data mining and statistical analysis to cite a few. A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. Among the mining techniques proposed so far, cluster analysis has become a standard method for the analysis of microarray expression data. It can be used both for initial screening of patients and for extraction of disease molecular signatures. Moreover, clustering can be profitably exploited to characterize genes of unknown function and uncover patterns that can be interpreted as indications of the status of cellular processes. Finally, clustering biological data would be useful not only for exploring the data but also for discovering implicit links between the objects. Indeed, a key feature that lacks in many proposed approach is the biological interpretation of the obtained results. In this paper, we will discuss such an issue by analysing the results obtained by several clustering algorithms w.r.t. their biological relevance. © 2014 Springer International Publishing.