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Computational approaches for the classification of seed storage proteins

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Title Computational approaches for the classification of seed storage proteins
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Creator V. Radhika
 
Subject Classification, Nearest neighbour algorithm, Correlation based feature selection, Machine learning, Seed storage proteins, Bio-informatics
 
Description Not Available
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.
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Date 2019-05-28T05:53:22Z
2019-05-28T05:53:22Z
2015-07-01
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/19931
 
Language English
 
Relation Not Available;
 
Publisher Not Available