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Big data, biostatistics and complexity reduction
Jan Kalina
Jazyk angličtina Země Česko
Typ dokumentu práce podpořená grantem
- MeSH
- analýza dat MeSH
- analýza hlavních komponent metody MeSH
- big data * MeSH
- biostatistika * metody MeSH
- kardiovaskulární nemoci genetika prevence a kontrola MeSH
- lidé MeSH
- metoda nejmenších čtverců MeSH
- riziko MeSH
- rozpoznání obličeje MeSH
- systémy pro podporu klinického rozhodování MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
The aim of this paper is to overview challenges and principles of Big Data analysis in biomedicine. Recent multivariate statistical approaches to complexity reduction represent a useful (and often irreplaceable) methodology allowing performing a reliable Big Data analysis. Attention is paid to principal component analysis, partial least squares, and variable selection based on maximizing conditional entropy. Some important problems as well as ideas of complexity reduction are illustrated on examples from biomedical research tasks. These include high-dimensional data in the form of facial images or gene expression measurements from a cardiovascular genetic study.
Citace poskytuje Crossref.org
Jana Zvárová Memorial Conference, 4 May 2018 in Prague
Bibliografie atd.Literatura
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