Semi-automatic mining of correlated data from a complex database: Correlation network visualization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00092617" target="_blank" >RIV/00216224:14330/16:00092617 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7802783/" target="_blank" >http://ieeexplore.ieee.org/document/7802783/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCABS.2016.7802783" target="_blank" >10.1109/ICCABS.2016.7802783</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Semi-automatic mining of correlated data from a complex database: Correlation network visualization

Popis výsledku v původním jazyce

In previous work we have addressed the issue of frequent ad-hoc queries in deeply-structured databases. We wrote a library of functions AutodenormLib.py for issuing proper JOIN commands to denormalize an arbitrary subset of stored data for downstream processing. This may include statistical analysis, visualization or machine learning. Here, we visualize the content of the Thalamoss biomedical database as a correlation network. The network is created by calculating pairwise correlations through all pairs of variables, whether they be numerical, ordinal or nominal. We subsequently construct the network over the entire set of variables, clustering variables with similar effects to discover group relationships between the various biomedical characteristics. We use a semi-automatic procedure that makes the selection of all pairs possible and discuss issues of dealing with different types of variables.

Název v anglickém jazyce

Semi-automatic mining of correlated data from a complex database: Correlation network visualization

Popis výsledku anglicky

In previous work we have addressed the issue of frequent ad-hoc queries in deeply-structured databases. We wrote a library of functions AutodenormLib.py for issuing proper JOIN commands to denormalize an arbitrary subset of stored data for downstream processing. This may include statistical analysis, visualization or machine learning. Here, we visualize the content of the Thalamoss biomedical database as a correlation network. The network is created by calculating pairwise correlations through all pairs of variables, whether they be numerical, ordinal or nominal. We subsequently construct the network over the entire set of variables, clustering variables with similar effects to discover group relationships between the various biomedical characteristics. We use a semi-automatic procedure that makes the selection of all pairs possible and discuss issues of dealing with different types of variables.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/7E13011" target="_blank" >7E13011: THALAssaemia MOdular Stratification System for personalized therapy of beta-thalassemia</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computational Advances in Bio and Medical Sciences (ICCABS), 2016 IEEE 6th International Conference on

ISBN

9781509041992

ISSN

2473-4659

e-ISSN

—

Počet stran výsledku

2

Strana od-do

1-2

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Atlanta, GA, USA

Datum konání akce

13. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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