Big Data, Biostatistics and Complexity Reduction

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00489389" target="_blank" >RIV/67985807:_____/18:00489389 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.24105/ejbi.2018.14.2.5" target="_blank" >http://dx.doi.org/10.24105/ejbi.2018.14.2.5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.24105/ejbi.2018.14.2.5" target="_blank" >10.24105/ejbi.2018.14.2.5</a>

Result language
angličtina
Original language name
Big Data, Biostatistics and Complexity Reduction
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/NV15-29835A" target="_blank" >NV15-29835A: Graph-theory approach to complex organization and dynamics of human epileptic networks: implications for epilepsy surgery planning.</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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