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Please use this identifier to cite or link to this item: http://krishi.icar.gov.in/jspui/handle/123456789/68728
Title: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel
Other Titles: Not Available
Authors: Prabina Kumar Meher
Subhrajit Satpathy
Anuj Sharma
Isha Saini
Sukanta Kumar Pradhan
Anil Rai
ICAR Data Use Licennce: http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf
Author's Affiliated institute: ICAR::Indian Agricultural Statistics Research Institute
Orissa University of Agriculture and Technology, Bhubaneswar, Odisha, India
Uttarakhand Council for Biotechnology, Pantnagar, Uttarakhand, India
Published/ Complete Date: 2021-04-26
Project Code: Not Available
Keywords: Circadian clock
Circadian rhythms
Circadian genes
Computational biology
Machine learning
Publisher: BioMed Central Ltd.
Citation: Meher, P.K., Mohapatra, A., Satpathy, S. et al. PredCRG: A computational method for recognition of plant circadian genes by employing support vector machine with Laplace kernel. Plant Methods 17, 46 (2021). https://doi.org/10.1186/s13007-021-00744-3
Series/Report no.: Not Available;
Abstract/Description: Circadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes. Support vector machine (SVM) with seven kernels, i.e., linear, polynomial, radial, sigmoid, hyperbolic, Bessel and Laplace was utilized for prediction by employing compositional, transitional and physico-chemical features. Higher accuracy of 62.48% was achieved with the Laplace kernel, following the fivefold cross- validation approach. The developed model further secured 62.96% accuracy with an independent dataset. The SVM also outperformed other state-of-art machine learning algorithms, i.e., Random Forest, Bagging, AdaBoost, XGBoost and LASSO. We also performed proteome-wide identification of circadian proteins in two cereal crops namely, Oryza sativa and Sorghum bicolor, followed by the functional annotation of the predicted circadian proteins with Gene Ontology (GO) terms. To the best of our knowledge, this is the first computational method to identify the circadian genes with the sequence data. Based on the proposed method, we have developed an R-package PredCRG (https:// cran.rproject. org/ web/ packa ges/ PredC RG/ index. html) for the scientific community for proteome-wide identification of circadian genes. The present study supplements the existing computational methods as well as wet-lab experiments for the recognition of circadian genes.
Description: Not Available
ISSN: 17464811
Type(s) of content: Article
Sponsors: Not Available
Language: English
Name of Journal: Plant Methods
Journal Type: Biotechnology (Q1); Plant Science (Q1); Genetics (Q2)
NAAS Rating: 10.993
Impact Factor: 4.993
Volume No.: 17
Page Number: 46
Name of the Division/Regional Station: Statistical Genetics
Source, DOI or any other URL: https://doi.org/10.1186/s13007-021-00744-3
URI: http://krishi.icar.gov.in/jspui/handle/123456789/68728
Appears in Collections:AEdu-IASRI-Publication

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