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ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features

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Title ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features
 
Creator Prabina Kumar Meher
Shbana Begam
Tanmaya Kumar Sahu
Ajit Gupta
Anuj Kumar
Upendra Kumar
Atmakuri Ramakrishna Rao
Krishna Pal Singh
Om Parkash Dhankher
 
Subject abiotic stress
miRNAs
stress-responsive genes
machine learning
computational biology
 
Description MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses.
Thus, identification of 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 method for prediction of miRNAs associated with abiotic stresses.
Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA.
The pseudo K-tuple nucleotide compositional features were generated for each sequence to transform
the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for
prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94
and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for
miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies
for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets.
The SVM also achieved higher accuracy than other learning methods such as random forest, extreme
gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction
server “ASRmiRNA” has been developed. The proposed approach is believed to supplement the
existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.
Not Available
 
Date 2022-05-25T08:42:36Z
2022-05-25T08:42:36Z
2022-01-30
 
Type Research Paper
 
Identifier Meher, Prabina K., Shbana Begam, Tanmaya K. Sahu, Ajit Gupta, Anuj Kumar, Upendra Kumar, Atmakuri R. Rao, Krishna P. Singh, and Om P. Dhankher. (2022). "ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features" International Journal of Molecular Sciences 23(3), 1612. https://doi.org/10.3390/ijms23031612
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/72390
 
Language English
 
Relation Not Available;
 
Publisher MDPI