Margherita Squillario

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MOTIVATION The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the(More)
BACKGROUND A molecular characterization of Alzheimer's Disease (AD) is the key to the identification of altered gene sets that lead to AD progression. We rely on the assumption that candidate marker genes for a given disease belong to specific pathogenic pathways, and we aim at unveiling those pathways stable across tissues, treatments and measurement(More)
• a typical scenario is n<<d • number of samples cannot always be increased (rare diseases and expensive technology) • (mostly) high-throughput data ✤ new technologies (DNA microarrays, CGH, SNP, etc.) ✤ possibility to measure the whole genome ✤ most of the times the data are noisy (getting better any day now..) biological samples microarray gene expression(More)
In computational biology, the analysis of high-throughput data poses several issues on the reliability, reproducibility and interpretabil-ity of the results. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the(More)
High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of(More)
From paired blood and spleen samples from three adult donors, we performed high-throughput VH sequencing of human B cell subsets defined by IgD and CD27 expression: IgD(+)CD27(+) ("marginal zone [MZ]"), IgD(-)CD27(+) ("memory," including IgM ["IgM-only"], IgG and IgA) and IgD(-)CD27(-) cells ("double-negative," including IgM, IgG, and IgA). A total of(More)
Biological interpretability is a key requirement for the output of microarray data analysis pipelines. The most used pipeline first identifies a gene signature from the acquired measurements and then uses gene enrichment analysis as a tool for functionally characterizing the obtained results. Recently Knowledge Driven Variable Selection (KDVS), an(More)
Motivation: A bioinformatics platform is introduced aimed at identifying models of disease-specific pathways, as well as a set of network measures that can quantify changes in terms of global structure or single link disruptions.The approach integrates a network comparison framework with machine learning molecular profiling. The platform includes different(More)
The great amount of information produced in the field of molecular biology and genetics opened enormous possibilities for life science researchers to analyze and understand biological phenomenons. This wealth, however, brings new problems, such as how to integrate existing information sources, and how to use already well-developed statistical techniques in(More)