Title
SubmitoLoc: Identification of mitochondrial sub cellular locations of proteins using support vector machine
Authors
Varadharaju Nithya
Affiliation
Department of Animal Health Management, Alagappa University, Karaikudi-630003, India
Varadharaju Nithya - E-mail: dr.nithya.gopinath@gmail.com
Article Type
Research Article
Date
Received December 28, 2019; Revised December 31, 2019; Accepted December 31, 2019; Published December 31, 2019
Abstract
Mitochondria are important subcellular organelles in eukaryotes. Defects in mitochondrial system lead to a variety of disease. Therefore, detailed knowledge of mitochondrial proteome is vital to understand mitochondrial system and their function. Sequence databases contain large number of mitochondrial proteins but they are mostly not annotated. In this study, we developed a support vector machine approach, SubmitoLoc, to predict mitochondrial sub cellular locations of proteins based on various sequence derived properties. We evaluated the predictor using 10-fold cross validation. Our method achieved 88.56 % accuracy using all features. Average sensitivity and specificity for four-subclass prediction is 85.37% and 87.25% respectively. High prediction accuracy suggests that SubmitoLoc will be useful for researchers studying mitochondrial biology and drug discovery.
Keywords
SVM, sub mitochondrial, protein prediction
Citation
Nithya, Bioinformation 15(12): 863-868 (2019)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
