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1. Di Camillo B, Martini M, Toffolo G A simulator of gene regulatory network evolution for validation of biomarker identification methods Meeting: BITS 2009 - Year: 2009 Full text in a new tab Topic: Transcriptomics Gene Expression and Microarray Analysis Abstract: Missing |
2. Di Camillo B, Nair KS, Toffolo G, Cobelli C Identification of gene regulatory modules using entropy and mutual information Meeting: BITS 2005 - Year: 2005 Full text in a new tab Topic: Unspecified Abstract: A crucial issue in microarray studies is the elucidation of how genes change expression and interact as a consequence of external/internal stimuli such as illness, drug assumption, hormone stimulation. To do so one has to reconstruct the regulatory network by describing activation/inhibition and cause-effect relationships among expression profiles. Different approaches are available in literature, but the small number of available samples with respect to the number of genes constitutes a major drawback to apply these methods to real microarray data. At present, a realistic aim is the identification of modules of gene regulation, i.e. sets of genes that are possibly regulated by the same transcription factors, or potential inhibitors or activators of a group of co-expressed genes. |
3. Di Camillo B, Toffolo G, Cobelli C A transcription network simulator Meeting: BITS 2007 - Year: 2007 Full text in a new tab Topic: Gene expression and system biology Abstract: Missing |
4. Di Camillo B, Toffolo G, Cobelli C Bayesian inference of transcriptional networks from gene temporal expression patterns Meeting: BITS 2007 - Year: 2007 Full text in a new tab Topic: Gene expression and system biology Abstract: Missing |
5. Di Camillo B, Toffolo G, Cobelli C Significance analysis of microarray transcript levels in time series experiments Meeting: BITS 2006 - Year: 2006 Full text in a new tab Topic: Microarray design and data analysis Abstract: Missing |
6. Di Camillo B, Toffolo G, Cobelli C, Nair KS Selection of Insulin Regulated Gene Expression Profiles Based on Intensity-Dependent Noise Distribution of Microarray Data Meeting: BITS 2004 - Year: 2004 Full text in a new tab Topic: Unspecified Abstract: Insulin resistance in skeletal muscle plays a key role in the development of Type 2 diabetes. To define the molecular mechanisms underlying insulin-induced changes in gene expression, recent studies, performed using microarrays techniques, identified genes involved in insulin resistance in control vs diabetic subjects, before vs after insulin treatment, i.e. exploiting only steady state information. Although extremely useful in order to identify candidate genes involved in analyzed processes and to develop new physiological hypothesis, these data can tell little about the interactions among genes. To infer genes regulation, it is of paramount importance to monitor dynamic expression profiles, i.e. time-series of expression data collected during the transition from one physiological state to another. A first necessary step, in order to limit the analysis to those genes that actually change expression over time, is to select differentially expressed genes. Methods proposed in the literature usually deal with comparison of static conditions rather than time-course experiment data, and are based on application of modified t-test and ANOVA test which assume Gaussian distribution of analyzed variables. These methods test the significance of the differential expression gene by gene, and their application requires at least two replicated experiments per each condition. In time course experiments, a number of samples is monitored across time and complete replicates of the experiment are seldom available, mainly for cost reasons. Therefore, differentially expressed genes are often selected using an empirical fold change (FC) threshold. This is a far-from-ideal situation, since it is based on an arbitrary choice (e.g. FC=2). In the case of Affymetrix chips, this choice is even more questionable since a constant threshold does not take in account the intensity dependence of the measurement errors, which is a wellknown feature of this technology.. Here, we propose a novel method for gene selection, to be applied on dynamic gene expression profiles, which explicitly accounts for the properties of the measurement errors and addresses the situation where a relative small number of replicates is available. |
7. Finotello F, Peruzzo D, Lavezzo E, Di Camillo B, Toffolo G, Cobelli C, Toppo S Complete and comparative analysis of algorithms for whole genome shotgun assembly Meeting: Proceedings of BITS 2010 Meeting - Year: 2010 Full text in a new tab Topic: New tools for NGS Abstract: Missing |
8. Nasso S, Silvestri F, Tisiot F, Di Camillo B, Pietracaprina A, Toffolo GM An Optimized Data Structure For High-Throughput 3D Proteomics Data: mzRTree Meeting: Proceedings of BITS 2010 Meeting - Year: 2010 Full text in a new tab Topic: Proteomics Abstract: Missing |