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OJBTM
Online Journal of Bioinformatics ©
Volume
14 (1): 51-55, 2013
Amino acid composition model
for prediction and identification of Alpha and Epsilon-proteobacteria.
Anuja Shanker, Kamal Raj Pardasani.
Department
of Mathematics, Bioinformatics & Computer Α-plications. MANIT, Bhopal, India.
ABSTRACT
Shanker A, Pardasani KR., Amino acid
composition model for prediction and identification of α and ˆ--proteobacteria, Online J Bioinform., 14 (1): 51-55, 2013. Alpha (α)-proteobacteria are thought to be the precursors of
mitochondria. Epsilon (ˆ-)-proteobacteria are either
symbionts or pathogens in animals. Therefore assigning correct taxonomic
identifiers to these organisms is important.
A model to predict, classify and distinguish proteobacteria
subclasses from other microbial species is described. Simulations using Amino
Acid Composition (AAC)
in a support vector machine using LibSVM
and SVM light programs were used to obtain an accuracy of 90%. The finding
suggests that AAC could be used as a parameter for prediction and annotation of
genomic and proteomic data.
Keywords: Signature proteins; Support vector
machine; Amino Acid composition; Kernel functions.
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