%0 Journal Article
%T Insights into the classification of small GTPases
%A Dominik Heider
%A Sascha Hauke
%A Martin Pyka
%A et al
%J Advances and Applications in Bioinformatics and Chemistry
%D 2010
%I Dove Medical Press
%R http://dx.doi.org/10.2147/AABC.S8891
%X sights into the classification of small GTPases Original Research (6567) Total Article Views Authors: Dominik Heider, Sascha Hauke, Martin Pyka, et al Published Date May 2010 Volume 2010:3 Pages 15 - 24 DOI: http://dx.doi.org/10.2147/AABC.S8891 Dominik Heider1, Sascha Hauke3, Martin Pyka4, Daniel Kessler2 1Department of Bioinformatics, Center for Medical Biotechnology, 2Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany; 3Institute of Computer Science, University of M¨šnster, M¨šnster, Germany; 4Interdisciplinary Center for Clinical Research, University Hospital of M¨šnster, M¨šnster, Germany Abstract: In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.
%K cancer
%K machine learning
%K classification
%K Random Forests
%K proteins
%U https://www.dovepress.com/insights-into-the-classification-of-small-gtpases-peer-reviewed-article-AABC