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Development of new bioinformatics tools for the analysis of transcriptional profiles in human cells


The interpretation of the transcriptional state of the cell and its alterations in specific experimental or pathological conditions is today of particular interest and several technologies have been developed to identify and quantify the entire set of cellular transcripts. As a consequence, the amount of gene expression data available in public repositories has grown exponentially in the last years, now requiring new data mining tools to extract biologically relevant information.


In this context we developed CorrelaGenes, a new bioinformatics tool exploiting GEO expression data to provide new insights about the pathways in which a gene of interest could be involved. CorrelaGenes is aimed at identifying lists of genes potentially correlated to a gene of interest. This is accomplished through a customized Association Rule Mining (ARM) algorithm allowing a cross-sectional analysis among data from different microarrays studies with the ultimate goal of detecting those genes showing modulation of their expression in a significant number of different conditions. 

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