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GIpred: a computational tool for prediction of GIGANTEA proteins using machine learning algorithm

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Title GIpred: a computational tool for prediction of GIGANTEA proteins using machine learning algorithm
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Creator Prabina Kumar Meher
Sagarika Dash
Tanmaya Kumar Sahu
Subhrajit Satpathy
Sukanta Kumar Pradhan
 
Subject GIGANTEA
protein
machine learning algorithm
 
Description Not Available
In plants, GIGANTEA (GI) protein plays different biological functions including carbon and sucrose metabolism, cell wall deposition, transpiration and hypocotyl elongation. This suggests that GI is an important class of proteins. So far, the resource-intensive experimental methods have been mostly utilized for identification of GI proteins. Thus, we made an attempt in this study to develop a computational model for fast and accurate prediction of GI proteins. Ten different supervised learning algorithms i.e., SVM, RF, JRIP, J48, LMT, IBK, NB, PART, BAGG and LGB were employed for prediction, where the amino acid composition (AAC), FASGAI features and physico-chemical (PHYC) properties were used as numerical inputs for the learning algorithms. Higher accuracies i.e., 96.75% of AUC-ROC and 86.7% of AUC-PR were observed for SVM coupled with AAC + PHYC feature combination, while evaluated with five-fold cross validation. With leave-one-out cross validation, 97.29% of AUC-ROC and 87.89% of AUC-PR were respectively achieved. While the performance of the model was evaluated with an independent dataset of 18 GI sequences, 17 were observed as correctly predicted. We have also performed proteome-wide identification of GI proteins in wheat, followed by functional annotation using Gene Ontology terms. A prediction server “GIpred” is freely accessible at http://cabgrid.res.in:8080/gipred/ for proteome-wide recognition of GI proteins.
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Date 2022-05-25T06:24:45Z
2022-05-25T06:24:45Z
2022-01-24
 
Type Research Paper
 
Identifier Meher, P.K., Dash, S., Sahu, T.K. et al. (2022). GIpred: a computational tool for prediction of GIGANTEA proteins using machine learning algorithm. Physiol Mol Biol Plants 28, 1–16 (2022). https://doi.org/10.1007/s12298-022-01130-6
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http://krishi.icar.gov.in/jspui/handle/123456789/72379
 
Language English
 
Relation Not Available;
 
Publisher Springer India