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Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 4, p e0195636 (2018)
- Publication Year :
- 2018
- Publisher :
- Public Library of Science, 2018.
-
Abstract
- A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate. Specifically, based on the position-specific scoring matrix (PSSM), we proposed a new expression, correlation position-specific scoring matrix (CoPSSM) as the protein feature representation. Based on the classic nonlinear dimension reduction algorithm, kernel linear discriminant analysis (KLDA), we added a new discriminant criterion and proposed a dichotomous greedy genetic algorithm (DGGA) to intelligently select its kernel bandwidth parameter. Two public datasets with Jackknife test and KNN classifier were used for the numerical experiments. The results showed that the overall success rate (OSR) with single representation CoPSSM is larger than that with many relevant representations. The OSR of the proposed method can reach as high as 87.444% and 90.3361% for these two datasets, respectively, outperforming many current methods. To show the generalization of the proposed algorithm, two extra standard datasets of protein subcellular were chosen to conduct the expending experiment, and the prediction accuracy by Jackknife test and Independent test is still considerable.
- Subjects :
- 0301 basic medicine
Generalization
Computer science
Intelligence
lcsh:Medicine
Social Sciences
02 engineering and technology
Protein Sequencing
Kernel Bandwidth
Linear Discriminant Analysis
Mathematical and Statistical Techniques
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
Psychology
lcsh:Science
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Discriminant Analysis
Protein Transport
Kernel (statistics)
Physical Sciences
020201 artificial intelligence & image processing
Information Technology
Statistics (Mathematics)
Algorithms
Research Article
Optimization
Computer and Information Sciences
Research and Analysis Methods
03 medical and health sciences
Kernel (linear algebra)
Artificial Intelligence
Genetic algorithm
Statistical Methods
Representation (mathematics)
Molecular Biology Techniques
Sequencing Techniques
Molecular Biology
Cell Nucleus
business.industry
Genetic Algorithms
lcsh:R
Cognitive Psychology
Biology and Life Sciences
Computational Biology
Proteins
Pattern recognition
Linear discriminant analysis
Data Reduction
030104 developmental biology
Discriminant
Linear Models
Cognitive Science
lcsh:Q
Artificial intelligence
business
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....af4d7a966bb542648b3ad2a742582a8b