Statistical strategies and stochastic predictive models for the MARK-AGE data
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MARK-AGE aims at the identification of biomarkers of human aging capable of discriminating between the chronological age and the effective functional status of the organism. To achieve this, given the structure of the collected data, a proper statistical analysis has to be performed, as the structure of the data are non trivial and the number of features under study is near to the number of subjects used, requiring special care to avoid overfitting. Here we described some of the possible strategies suitable for this analysis. We also include a description of the main techniques used, to explain and justify the selected strategies. Among other possibilities, we suggest to model and analyze the data with a three step strategy.
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GIAMPIERI, Enrico, Daniel REMONDINI, Maria Giulia BACALINI, Paolo GARAGNANI, Chiara PIRAZZINI, Stella Lukas YANI, Cristina GIULIANI, Giulia MENICHETTI, Isabella ZIRONI, Claudia SALA, Miriam CAPRI, Claudio FRANCESCHI, Alexander BÜRKLE, Gastone CASTELLANI, 2015. Statistical strategies and stochastic predictive models for the MARK-AGE data. In: Mechanisms of Ageing and Development. 2015, 151, pp. 45-53. ISSN 0047-6374. eISSN 1872-6216. Available under: doi: 10.1016/j.mad.2015.07.001BibTex
@article{Giampieri2015Stati-33339, year={2015}, doi={10.1016/j.mad.2015.07.001}, title={Statistical strategies and stochastic predictive models for the MARK-AGE data}, volume={151}, issn={0047-6374}, journal={Mechanisms of Ageing and Development}, pages={45--53}, author={Giampieri, Enrico and Remondini, Daniel and Bacalini, Maria Giulia and Garagnani, Paolo and Pirazzini, Chiara and Yani, Stella Lukas and Giuliani, Cristina and Menichetti, Giulia and Zironi, Isabella and Sala, Claudia and Capri, Miriam and Franceschi, Claudio and Bürkle, Alexander and Castellani, Gastone} }
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