Dario Martucci

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Statistical and clustering analyses of gene expression results from high-density microarray experiments produce lists of hundreds of genes regulated differentially, or with particular expression profiles, in the conditions under study. Independent of the microarray platforms and analysis methods used, these lists must be biologically interpreted to gain a(More)
While a massive amount of biomolecular information is increasingly accumulating in different databanks, on the other hand high-throughput technologies are generating a great quantity of data that need to be annotated with the genomic information available, and interpreted. To this aim, the use of specific ontologies can greatly help either in integrating(More)
Microarray technology is generating a massive amount of data that need to be studied with statistical approaches, clustered according to gene expression characteristics, and annotated with biological relevant information. Many web-based tools and databases have been created to annotate genes with biological information, classify genes in function-related(More)
New high-throughput technologies available for the post-genomic era, such as the microarrays, produce lists of hundreds of genes candidate regulated, or with particular expression profiles, in the conditions under study. These lists need to be biologically interpreted to gain a better knowledge of the pathophysiological phenomena involved. To this aim,(More)
Statistical and clustering analyses of gene expression results from high-throughput microarray experiments produce lists of hundreds of genes candidate regulated, or with particular expression profile patterns, in the conditions under study. Independently of the microarray platforms and analysis methods used to identify and cluster differentially expressed(More)
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