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Radhika V, Rao VS. Computational approaches for the classification of seed storage proteins. J Food Sci Technol. 2014;52(7):4246-55doi: 10.1007/s13197-014-1500-x.
Radhika, V., & Rao, V. S. (2015). Computational approaches for the classification of seed storage proteins. Journal of food science and technology, 52(7), 4246-55. https://doi.org/10.1007/s13197-014-1500-x
Radhika, V, and Rao, V Sree Hari. "Computational approaches for the classification of seed storage proteins." Journal of food science and technology vol. 52,7 (2015): 4246-55. doi: https://doi.org/10.1007/s13197-014-1500-x
Radhika V, Rao VS. Computational approaches for the classification of seed storage proteins. J Food Sci Technol. 2015 Jul;52(7):4246-55. doi: 10.1007/s13197-014-1500-x. Epub 2014 Aug 14. PMID: 26139889; PMCID: PMC4486583.
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J Food Sci Technol. 2015 Jul;52(7):4246-55. doi: 10.1007/s13197-014-1500-x. Epub 2014 Aug 14.

Computational approaches for the classification of seed storage proteins.

Journal of food science and technology

V Radhika, V Sree Hari Rao

Affiliations

  1. Indian Institute of Horticultural Research, Hessaraghatta Lake P.O., Bangalore, 560 089 India.
  2. Foundation for Scientific Research and Technological Innovations, Hyderabad, 500 035 A.P India.

PMID: 26139889 PMCID: PMC4486583 DOI: 10.1007/s13197-014-1500-x

Abstract

Seed storage proteins comprise a major part of the protein content of the seed and have an important role on the quality of the seed. These storage proteins are important because they determine the total protein content and have an effect on the nutritional quality and functional properties for food processing. Transgenic plants are being used to develop improved lines for incorporation into plant breeding programs and the nutrient composition of seeds is a major target of molecular breeding programs. Hence, classification of these proteins is crucial for the development of superior varieties with improved nutritional quality. In this study we have applied machine learning algorithms for classification of seed storage proteins. We have presented an algorithm based on nearest neighbor approach for classification of seed storage proteins and compared its performance with decision tree J48, multilayer perceptron neural (MLP) network and support vector machine (SVM) libSVM. The model based on our algorithm has been able to give higher classification accuracy in comparison to the other methods.

Keywords: Bio-informatics; Classification; Correlation based feature selection; Machine learning; Nearest neighbour algorithm; Seed storage proteins

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