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A Novel Algorithm for Prediction of Hub Proteins from Primary Structure in Eukaryotic Proteome Using Dipeptide Compositional Skew Information and Amino Acid Sequence Likeness

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Prospects in Bioscience: Addressing the Issues

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Abstract

We propose a novel hub-finding algorithm which relies on the use of dipeptide composition and amino acid sequence likeness. For extracting the most prominent features in hub identification, two feature selection techniques are widely used in data preprocessing for machine learning problems: fast correlation-based feature selection (FCBFS) and correlation-based feature selection (CFS) algorithms. The performance of two types of classifiers such as random forest classifier (RFC) and RBF network was evaluated with these filter approaches. Our proposed model led to successful prediction of hub proteins from primary structure with 92.52 and 91.28% accuracy for RFC and RBF network, respectively, in case of FCBFS and 90.92 and 93.76% accuracy for RFC and RBF network, respectively, in case of CFS algorithm.

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Authors and Affiliations

  1. Department of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram, 695581, India

    B. L. Aswathi, Baharak Goli, Renganayaki Govindarajan & Achuthsankar S. Nair

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  1. B. L. Aswathi
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  2. Baharak Goli
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  3. Renganayaki Govindarajan
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  4. Achuthsankar S. Nair
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Correspondence to B. L. Aswathi .

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Editors and Affiliations

  1. , Dept of Biotechnology & Microbiology, Kannur University, Palayad P.O,Thalassery Campus, Kannur, 670661, Kerala, India

    Abdulhameed Sabu

  2. , Dept. of Biotechnology and Microbiology, Kannur University, Palayad P.O, Thalassery Campus, Kannur, 670661, Kerala, India

    Anu Augustine

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Aswathi, B.L., Goli, B., Govindarajan, R., Nair, A.S. (2012). A Novel Algorithm for Prediction of Hub Proteins from Primary Structure in Eukaryotic Proteome Using Dipeptide Compositional Skew Information and Amino Acid Sequence Likeness. In: Sabu, A., Augustine, A. (eds) Prospects in Bioscience: Addressing the Issues. Springer, India. https://doi.org/10.1007/978-81-322-0810-5_4

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