Statistical strategies and stochastic predictive models for the MARK-AGE data

Enrico Giampieri, 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

Research output: Contribution to journalArticle

Abstract

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:.

Original languageEnglish
Pages (from-to)45-53
Number of pages9
JournalMechanisms of Ageing and Development
Volume151
DOIs
Publication statusPublished - Dec 2 2014

Keywords

  • Biological age
  • Biomarkers
  • Chronological age
  • MARK-AGE
  • Statistics models

ASJC Scopus subject areas

  • Ageing
  • Developmental Biology

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  • Cite this

    Giampieri, E., Remondini, D., Bacalini, M. G., Garagnani, P., Pirazzini, C., Yani, S. L., Giuliani, C., Menichetti, G., Zironi, I., Sala, C., Capri, M., Franceschi, C., Bürkle, A., & Castellani, G. (2014). Statistical strategies and stochastic predictive models for the MARK-AGE data. Mechanisms of Ageing and Development, 151, 45-53. https://doi.org/10.1016/j.mad.2015.07.001