P. G. Majumdar, A. Rao, Amit Kairi, Prabina Kumar Meher, Sarika Sahu
{"title":"植物编码和非编码rna的高效学习分类器的鉴定","authors":"P. G. Majumdar, A. Rao, Amit Kairi, Prabina Kumar Meher, Sarika Sahu","doi":"10.31742/isgpb.82.3.2","DOIUrl":null,"url":null,"abstract":"\n\n\n\nThough 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.\n\n\n\n","PeriodicalId":13321,"journal":{"name":"Indian Journal of Genetics and Plant Breeding","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of efficient learning classifiers for discrimination of coding and non-coding RNAs in plant species\",\"authors\":\"P. G. Majumdar, A. Rao, Amit Kairi, Prabina Kumar Meher, Sarika Sahu\",\"doi\":\"10.31742/isgpb.82.3.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\n\\n\\nThough 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.\\n\\n\\n\\n\",\"PeriodicalId\":13321,\"journal\":{\"name\":\"Indian Journal of Genetics and Plant Breeding\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2022-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indian Journal of Genetics and Plant Breeding\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.31742/isgpb.82.3.2\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Genetics and Plant Breeding","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.31742/isgpb.82.3.2","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
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.
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.