植物编码和非编码rna的高效学习分类器的鉴定

IF 1 4区 生物学 Q3 PLANT SCIENCES Indian Journal of Genetics and Plant Breeding Pub Date : 2022-09-30 DOI:10.31742/isgpb.82.3.2
P. G. Majumdar, A. Rao, Amit Kairi, Prabina Kumar Meher, Sarika Sahu
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引用次数: 0

摘要

尽管非编码RNA(ncRNA)不编码蛋白质,但它们除了参与致病机制和表观遗传学机制外,还作为功能性RNA调节基因表达。因此,区分ncRNA和编码RNA(cRNA)在转录组研究中很重要。包括深度学习分类器在内的几种基于机器学习的分类器已被用于区分cRNAs和ncRNAs。然而,这种分类器在植物物种中的性能比较尚待确定。因此,在本研究中,使用水稻、小麦、玉米、棉花、向日葵、大麦、香蕉、葡萄、木瓜等植物物种的数据集,评估了深度神经网络(DNN)、随机森林(RF)、支持向量机(SVM)和人工神经网络(ANN)等分类器对cRNA和ncRNA进行分类的性能。此外,通过遵循交叉验证过程以及考虑3997个cRNA和4110个ncRNA的独立测试数据集来评估分类器的性能。结果表明,随机森林分类器在机器学习分类器中表现出最高的性能准确率(99.803%),其次是DNN(99.519%)、SVM(97.364%)和ANN(99.260%)。本研究有望帮助计算和实验生物学家轻松区分编码和非编码RNA。
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Identification of efficient learning classifiers for discrimination of coding and non-coding RNAs in plant species
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.
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来源期刊
CiteScore
1.80
自引率
10.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Advance the cause of genetics and plant breeding and to encourage and promote study and research in these disciplines in the service of agriculture; to disseminate the knowledge of genetics and plant breeding; provide facilities for association and conference among students of genetics and plant breeding and for encouragement of close relationship between them and those in the related sciences; advocate policies in the interest of the nation in the field of genetics and plant breeding, and facilitate international cooperation in the field of genetics and plant breeding.
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