Dehzangi, A. and Heffernan, R. and Sharma, Alokanand and Lyons, J. and Paliwal, K.K. and Sattar, A. (2015) Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳ s general PseAAC. Journal of Theoretical Biology, 364 . pp. 284-294. ISSN 0022-5193
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Abstract
Protein subcellular localization is defined as predicting the functioning location of a given protein in the
cell. It is considered an important step towards protein function prediction and drug design. Recent
studies have shown that relying on Gene Ontology (GO) for feature extraction can improve protein
subcellular localization prediction performance. However, relying solely on GO, this problem remains
unsolved. At the same time, the impact of other sources of features especially evolutionary-based
features has not been explored adequately for this task. In this study, we aim to extract discriminative
evolutionary features to tackle this problem. To do this, we propose two segmentation based feature
extraction methods to explore potential local evolutionary-based information for Gram-positive and
Gram-negative subcellular localizations. We will show that by applying a Support Vector Machine (SVM)
classifier to our extracted features, we are able to enhance Gram-positive and Gram-negative subcellular
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Alokanand Sharma |
| Date Deposited: | 12 Jan 2016 02:12 |
| Last Modified: | 26 Apr 2016 21:43 |
| URI: | https://repository.usp.ac.fj/id/eprint/8644 |
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