Prediction of DNA-Binding Proteins from Relational Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00201128" target="_blank" >RIV/68407700:21230/12:00201128 - isvavai.cz</a>
Result on the web
<a href="http://www.proteomesci.com/content/pdf/1477-5956-10-66.pdf" target="_blank" >http://www.proteomesci.com/content/pdf/1477-5956-10-66.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1186/1477-5956-10-66" target="_blank" >10.1186/1477-5956-10-66</a>
Alternative languages
Result language
angličtina
Original language name
Prediction of DNA-Binding Proteins from Relational Features
Original language description
The process of protein-DNA binding has an essential role in the biological processing of genetic information. We use relational machine learning to predict DNA-binding propensity of proteins from their structures. Automatically discovered structural features are able to capture some characteristic spatial configurations of amino acids in proteins. Prediction based only on structural relational features already achieves competitive results to existing methods based on physicochemical properties on several protein datasets. Predictive performance is further improved when structural features are combined with physicochemical features. Moreover, the structural features provide some insights not revealed by physicochemical features. Our method is able to detect common spatial substructures. We demonstrate this in experiments with zinc finger proteins.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
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
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Proteome Science
ISSN
1477-5956
e-ISSN
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Volume of the periodical
2012
Issue of the periodical within the volume
10
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
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UT code for WoS article
000315389600001
EID of the result in the Scopus database
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