| Summary: | Dominik Heider1, Sascha Hauke3, Martin Pyka4, Daniel Kessler21Department of Bioinformatics, Center for Medical Biotechnology, 2Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany; 3Institute of Computer Science, University of Münster, Münster, Germany; 4Interdisciplinary Center for Clinical Research, University Hospital of Münster, Münster, GermanyAbstract: 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.Keywords: cancer, machine learning, classification, Random Forests, proteins
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