Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine

Apoptosis proteins play an important role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. However, most of the existing predictive me...

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Bibliographic Details
Published in:Amino acids 2008-08, Vol.35 (2), p.383-388
Main Authors: Zhou, X.-B, Chen, C, Li, Z.-C, Zou, X.-Y
Format: Article
Language:English
Subjects:
Algorithms
amino acid composition
Amino Acid Sequence
Amino acids
Amphiphilic pseudo amino acid composition
Analytical Chemistry
Apoptosis
Apoptosis protein
Apoptosis Regulatory Proteins - chemistry
Artificial Intelligence
Biochemical Engineering
Biochemistry
Biomedical and Life Sciences
Cells
Computational Biology - methods
Computer Simulation
Databases, Protein
Dipeptide composition
Dual-layer support vector machine
Intracellular Space - chemistry
Life Sciences
Neurobiology
Organisms
Pattern Recognition, Automated - methods
Position (location)
Predictive Value of Tests
Proteins
Proteomics
Subcellular location
Support vector machines
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Summary:Apoptosis proteins play an important role in the development and homeostasis of an organism. The accurate prediction of subcellular location for apoptosis proteins is very helpful for understanding the mechanism of apoptosis and their biological functions. However, most of the existing predictive methods are designed by utilizing a single classifier, which would limit the further improvement of their performances. In this paper, a novel predictive method, which is essentially a multi-classifier system, has been proposed by combing a dual-layer support vector machine (SVM) with multiple compositions including amino acid composition (AAC), dipeptide composition (DPC) and amphiphilic pseudo amino acid composition (Am-Pse-AAC). As a demonstration, the predictive performance of our method was evaluated on two datasets of apoptosis proteins, involving the standard dataset ZD98 generated by Zhou and Doctor, and a larger dataset ZW225 generated by Zhang et al. With the jackknife test, the overall accuracies of our method on the two datasets reach 94.90% and 88.44%, respectively. The promising results indicate that our method can be a complementary tool for the prediction of subcellular location.
ISSN:0939-4451
1438-2199
DOI:10.1007/s00726-007-0608-y