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ASmiR: a machine learning framework for prediction of abiotic stress–specific miRNAs in plants

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Title ASmiR: a machine learning framework for prediction of abiotic stress–specific miRNAs in plants
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Creator Upendra Kumar Pradhan
Prabina Kumar Meher
Sanchita Naha
Atmakuri Ramakrishna Rao
Upendra Kumar
Soumen Pal
Ajit Gupta
 
Subject Abiotic stress
miRNAs
Machine learning
Deep learning
Computational biology
 
Description Not Available
Abiotic stresses have become a major challenge in recent years due to their pervasive nature and shocking impacts on plant growth, development, and quality. MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of specific abiotic stress–responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning–based computational model for prediction of miRNAs associated with four specific abiotic stresses such as cold, drought, heat and salt. The pseudo K-tuple nucleotide compositional features of Kmer size 1 to 5 were used to represent miRNAs in numeric form. Feature selection strategy was employed to select important features. With the selected feature sets, support vector machine (SVM) achieved the highest cross-validation accuracy in all four abiotic stress conditions. The highest cross-validated prediction accuracies in terms of area under precision-recall curve were found to be 90.15, 90.09, 87.71, and 89.25% for cold, drought, heat and salt respectively. Overall prediction accuracies for the independent dataset were respectively observed 84.57, 80.62, 80.38 and 82.78%, for the abiotic stresses. The SVM was also seen to outperform different deep learning models for prediction of abiotic stress–responsive miRNAs. To implement our method with ease, an online prediction server “ASmiR” has been established at https://iasri-sg.icar.gov.in/asmir/. The proposed computational model and the developed prediction tool are believed to supplement the existing effort for identification of specific abiotic stress–responsive miRNAs in plants.
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Date 2023-03-31T09:19:24Z
2023-03-31T09:19:24Z
2023-03-20
 
Type Article
 
Identifier Pradhan, U.K., Meher, P.K., Naha, S., Rao, A.R., Kumar, U., Pal, S., Gupta, A. (2023). ASmiR: a machine learning framework for prediction of abiotic stress–specific miRNAs in plants. Funct Integr Genomics, 23:92. https://doi.org/10.1007/s10142-023-01014-2
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http://krishi.icar.gov.in/jspui/handle/123456789/76642
 
Language English
 
Relation Not Available;
 
Publisher Springer