Gaussian Logic for Proteomics and Genomics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00181851" target="_blank" >RIV/68407700:21230/11:00181851 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Gaussian Logic for Proteomics and Genomics
Original language description
We describe a statistical relational learning framework called Gaussian Logic capable to work efficiently with combinations of relational and numerical data. The framework assumes that, for a fixed relational structure, the numerical data can be modelledby a multivariate normal distribution. We show how the Gaussian Logic framework can be used to predict DNA-binding propensity of proteins and to find motifs describing novel gene sets which are then used in set-level classification of gene expression sample.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 5th International Workshop on Machine Learning in Systems Biology
ISBN
978-1-4503-0796-3
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
44-48
Publisher name
Technical University of Munich
Place of publication
Munich
Event location
Vienna
Event date
Jul 20, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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