Big Data, Biostatistics and Complexity Reduction

0489389 - ÚI 2019 RIV CZ eng J - Journal Article
Kalina, Jan
Big Data, Biostatistics and Complexity Reduction.
European Journal for Biomedical Informatics. Roč. 14, č. 2 (2018), s. 24-32. ISSN 1801-5603
R&D Projects: GA MZd(CZ) NV15-29835A
Institutional support: RVO:67985807
Keywords : Biostatistics * Big data * Multivariate statistics * Dimensionality * Variable selection
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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.
Permanent Link: http://hdl.handle.net/11104/0283810