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

•The MARK-AGE project aims to develop a prediction model for the biological age.•A proper analysis pipeline is discussed in the light of the state of art.•It is fundamental to use robust estimators that acknowledge the structure of the data.•A train-test split division is necessary to avoid biases i...

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Vydáno v:Mechanisms of ageing and development Ročník 151; s. 45 - 53
Hlavní autoři: Giampieri, Enrico, Remondini, Daniel, Bacalini, Maria Giulia, Garagnani, Paolo, Pirazzini, Chiara, Yani, Stella Lukas, Giuliani, Cristina, Menichetti, Giulia, Zironi, Isabella, Sala, Claudia, Capri, Miriam, Franceschi, Claudio, Bürkle, Alexander, Castellani, Gastone
Médium: Journal Article
Jazyk:angličtina
Vydáno: Ireland Elsevier Ireland Ltd 01.11.2015
Témata:
Aging
Automatic Data Processing - methods
Biological age
Biomarkers
Chronological age
Databases, Factual
Female
Humans
Male
MARK-AGE
Models, Theoretical
Statistics models
Stochastic Processes
Biomarkers
Biological age
MARK-AGE
Statistics models
Chronological age
ISSN:0047-6374, 1872-6216, 1872-6216
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Popis
Shrnutí:•The MARK-AGE project aims to develop a prediction model for the biological age.•A proper analysis pipeline is discussed in the light of the state of art.•It is fundamental to use robust estimators that acknowledge the structure of the data.•A train-test split division is necessary to avoid biases in the prediction.•Bayesian methods that allow to include prior medical knowledge should be preferred. 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:
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0047-6374
1872-6216
1872-6216
DOI:10.1016/j.mad.2015.07.001