ARTIFICIAL NEURAL NETWORKS (ANNS) APPROACH FOR CLASSIFICATION OF SEED STORAGE PROTEINS OF VARIOUS NUTRITIONALLY SUPERIOR CEREAL CROPS
Journal Title: International Journal of Agriculture Sciences - Year 2017, Vol 9, Issue 5
Abstract
Seed storage proteins comprise a key part of the protein content and play pivotal role to maintain the quality of seed. The composition of storage proteins are very essential because they determine the total protein content of the seed and show their effect on nutritional quality of the seed as well as functional properties of food processing. Therefore, classification is required to categorize these proteins and for the development of crops with improved nutritional superior varieties. Bioinformatics tools and techniques are extensively employed in the arena of agriculture to annotate the biological data. Annotation uncovers the structural and functional characteristics of genes as well as proteins also. In present study seed storage proteins of five major cereal crops were categorized into four classes i.e. albumins (12), globulins (42), glutelins (11) and prolamins (68) using six physicochemical properties (number of amino acid, molecular weight, theoretical pI (isoelectric point), aliphatic index, instability index and hydropathicity) by employing Artificial Neural Networks (ANNs) approach.
Authors and Affiliations
A. K. MISHRA
Keywords
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