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Big Data, Biostatistics and Complexity Reduction
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SYSNO ASEP 0489389 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Big Data, Biostatistics and Complexity Reduction Author(s) Kalina, Jan (UIVT-O) RID, SAI, ORCID Source Title European Journal for Biomedical Informatics. - : Pulsus Group - ISSN 1801-5603
Roč. 14, č. 2 (2018), s. 24-32Number of pages 9 s. Language eng - English Country CZ - Czech Republic Keywords Biostatistics ; Big data ; Multivariate statistics ; Dimensionality ; Variable selection Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects NV15-29835A GA MZd - Ministry of Health (MZ) Institutional support UIVT-O - RVO:67985807 DOI 10.24105/ejbi.2018.14.2.5 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019
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