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Big data, biostatistics and complexity reduction
Jan Kalina
Language English Country Czech Republic
Document type Research Support, Non-U.S. Gov't
- MeSH
- Data Analysis MeSH
- Principal Component Analysis methods MeSH
- Big Data * MeSH
- Biostatistics * methods MeSH
- Cardiovascular Diseases genetics prevention & control MeSH
- Humans MeSH
- Least-Squares Analysis MeSH
- Risk MeSH
- Facial Recognition MeSH
- Decision Support Systems, Clinical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't 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.
References provided by Crossref.org
Jana Zvárová Memorial Conference, 4 May 2018 in Prague
Bibliography, etc.Literatura
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