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

• 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
• ISSN: 0273-2289; 1559-0291
• DOI: 10.1007/s12010-013-0268-1

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 %.