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Identification of efficient learning classifiers for discrimination of coding and non-coding RNAs in plant species

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Title Identification of efficient learning classifiers for discrimination of coding and non-coding RNAs in plant species
Not Available
 
Creator Priyanka Guha Majumdar
AR Rao
Amit Kairi
PK Meher
Sarika Sahu
 
Subject Coding RNAs
deep learning
machine learning
non-coding RNAs
 
Description Not Available
Though the non-coding RNAs (ncRNAs) do not encode for proteins, they act as functional RNAs and regulate gene expression besides
their involvement in disease-causing mechanisms and epigenetic mechanisms. Thus, discriminating ncRNAs from coding RNAs (cRNAs) is
important in transcriptome studies. Several machine learning-based classifiers, including deep learning classifiers, have been employed
for discriminating cRNAsfrom ncRNAs. However, the performance comparison of such classifiers in plant species is yet to be ascertained.
Thus, in the present study, the performance of the classifiers such as Deep Neural Network (DNN), Random Forest (RF), Support Vector
Machine (SVM), and Artificial Neural Network (ANN) were evaluated for classifying cRNAs and ncRNAsby using the datasets of plant
species including crops such as rice, wheat, maize, cotton, sunflower, barley, banana, grape, papaya. Further, the performance of
classifiers was assessed by following the cross-validation process as well as by considering an independent test data set of 3,997 cRNAs
and 4,110 ncRNAs. The results revealed that Random Forest classifier exhibited highest performance accuracy (99.803%) among the
machine learning classifiers, followed by DNN (99.519%), SVM (97.364%) and ANN (99.260%). The present study is expected to help
computational and experimental biologists for easy discrimination between coding and non-coding RNAs.
Not Available
 
Date 2023-05-09T03:33:23Z
2023-05-09T03:33:23Z
2022-09-30
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/76969
 
Language English
 
Relation Not Available;
 
Publisher Not Available