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Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
There are a lot of bacteria in the environment, and Gram-positive bacteria are the most common ones.
Some Gram-positive bacteria are very harmful to the human body, so it is significant to predict Gram-positive bacterial protein subcellular location.
And identification of Gram-positive bacterial protein subcellular location is important for developing effective drugs.
In this paper, a new Gram-positive bacterial protein subcellular location dataset was established.
The amino acid composition, the gene ontology annotation information, the hydropathy dipeptide composition information, the amino acid dipeptide composition information, and the autocovariance average chemical shift information were selected as characteristic parameters, then these parameters were combined.
The locations of Gram-positive bacterial proteins were predicted by the Support Vector Machine (SVM) algorithm, and the overall accuracy (OA) reached 86.1% under the Jackknife test.
The overall accuracy (OA) in our predictive model was higher than those in existing methods.
This improved method may be helpful for protein function prediction.
American Psychological Association (APA)
Li, Feng-Min& Gao, Xiao-Wei. 2020. Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features. BioMed Research International،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1138237
Modern Language Association (MLA)
Li, Feng-Min& Gao, Xiao-Wei. Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features. BioMed Research International No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1138237
American Medical Association (AMA)
Li, Feng-Min& Gao, Xiao-Wei. Predicting Gram-Positive Bacterial Protein Subcellular Location by Using Combined Features. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1138237
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138237