Application of Intelligent Techniques for Classification of Bacteria Using Protein Sequence-Derived Features

Standard molecular experimental methodologies and mathematical procedures often fail to answer many phylogeny and classification related issues. Modern artificial intelligent-based techniques, such as radial basis function, genetic algorithm, artificial neural network, and support vector machines ar...

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Bibliographic Details
Published in:Applied biochemistry and biotechnology 2013-07, Vol.170 (6), p.1263-1281
Main Authors: Banerjee, Amit Kumar, Ravi, Vadlamani, Murty, U. S. N., Sengupta, Neelava, Karuna, Batepatti
Format: Article
Language:English
Subjects:
Algorithms
Amino Acid Sequence
Amino acids
Artificial Intelligence
Bacteria
Bacteria - classification
Bacteria - enzymology
Bacterial Typing Techniques - methods
Biochemistry
Biological and medical sciences
Biotechnology
Chemistry
Chemistry and Materials Science
Classification
Data mining
Experimental methods
Fundamental and applied biological sciences. Psychology
Histidine Kinase
Molecular biology
Molecular Sequence Data
Phylogeny
Physicochemical properties
Protein Kinases - chemistry
Protein Kinases - metabolism
Proteins
Sequence Analysis, Protein - methods
Sulfur
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Summary:Standard molecular experimental methodologies and mathematical procedures often fail to answer many phylogeny and classification related issues. Modern artificial intelligent-based techniques, such as radial basis function, genetic algorithm, artificial neural network, and support vector machines are of ample potential in this regard. Reliance on a large number of essential parameters will aid in enhanced robustness, reliability, and better accuracy as opposed to single molecular parameter. This study was conducted with dataset of computed protein physicochemical properties belonging to 20 different bacterial genera. A total of 57 sequential and structural parameters derived from protein sequences were considered for the initial classification. Feature selection based techniques were employed to find out the most important features influencing the dataset. Various amino acids, hydrophobicity, relative sulfur percentage, and codon number were selected as important parameters during the study. Comparative analyses were performed applying RapidMiner data mining platform. Support vector machine proved to be the best method with maximum accuracy of more than 91 %.
ISSN:0273-2289
1559-0291
DOI:10.1007/s12010-013-0268-1