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Identification and Analysis of Driver Missense Mutations Using Rotation Forest with Feature Selection
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes.
Many approaches are proposed which use supervised machine learning techniques for prediction with features obtained by some databases.
However, often we do not know which features are important for driver mutations prediction.
In this study, we propose a novel feature selection method (called DX) from 126 candidate features’ set.
In order to obtain the best performance, rotation forest algorithm was adopted to perform the experiment.
On the train dataset which was collected from COSMIC and Swiss-Prot databases, we are able to obtain high prediction performance with 88.03% accuracy, 93.9% precision, and 81.35% recall when the 11 top-ranked features were used.
Comparison with other various techniques in the TP53, EGFR, and Cosmic2plus datasets shows the generality of our method.
American Psychological Association (APA)
Du, Xiuquan& Cheng, Jiaxing. 2014. Identification and Analysis of Driver Missense Mutations Using Rotation Forest with Feature Selection. BioMed Research International،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1016630
Modern Language Association (MLA)
Du, Xiuquan& Cheng, Jiaxing. Identification and Analysis of Driver Missense Mutations Using Rotation Forest with Feature Selection. BioMed Research International No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1016630
American Medical Association (AMA)
Du, Xiuquan& Cheng, Jiaxing. Identification and Analysis of Driver Missense Mutations Using Rotation Forest with Feature Selection. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1016630
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1016630