Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16619
Title: Feature Fusion-Based Food Protein Subcellular Prediction for Drug Composition
Authors: Byeon, Haewon
Shabaz, Mohammad
Ramesh, Janjhyam Venkata Naga
Dutta, Ashit Kumar
Vijay, Richa
Soni, Mukesh
Patni, Jagdish Chandra
Rusho, Maher Ali
Singh, Pavitar Parkash
Keywords: Drug Composition
Food Protein
Fusion Of Feature
Principal Component Analysis
Pseaac
Subcellular Prediction Of Proteins
Issue Date: 2024
Publisher: Food Chemistry
Elsevier Ltd
Citation: Vol. 454
Abstract: The structure and function of dietary proteins, as well as their subcellular prediction, are critical for designing and developing new drug compositions and understanding the pathophysiology of certain diseases. As a remedy, we provide a subcellular localization method based on feature fusion and clustering for dietary proteins. Additionally, an enhanced PseAAC (Pseudo-amino acid composition) method is suggested, which builds upon the conventional PseAAC. The study initially builds a novel model of representing the food protein sequence by integrating autocorrelation, chi density, and improved PseAAC to better convey information about the food protein sequence. After that, the dimensionality of the fused feature vectors is reduced by using principal component analysis. With prediction accuracies of 99.24% in the Gram-positive dataset and 95.33% in the Gram-negative dataset, respectively, the experimental findings demonstrate the practicability and efficacy of the proposed approach. This paper is basically exploring pseudo-amino acid composition of not any clinical aspect but exploring a pharmaceutical aspect for drug repositioning. © 2024 Elsevier Ltd
URI: https://doi.org/10.1016/j.foodchem.2024.139747
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16619
ISSN: 0308-8146
Appears in Collections:Journal Articles

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