Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees
MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predic...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Nucleic Acids |
| Online Access: | http://dx.doi.org/10.1155/2012/652979 |
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| _version_ | 1832548543387140096 |
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| author | Philip H. Williams Rod Eyles Georg Weiller |
| author_facet | Philip H. Williams Rod Eyles Georg Weiller |
| author_sort | Philip H. Williams |
| collection | DOAJ |
| description | MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require “read count” to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA∗ duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation. |
| format | Article |
| id | doaj-art-6ac5ceea3fba4c87b371be843c3242d9 |
| institution | Kabale University |
| issn | 2090-0201 2090-021X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Nucleic Acids |
| spelling | doaj-art-6ac5ceea3fba4c87b371be843c3242d92025-02-03T06:13:48ZengWileyJournal of Nucleic Acids2090-02012090-021X2012-01-01201210.1155/2012/652979652979Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision TreesPhilip H. Williams0Rod Eyles1Georg Weiller2Division of Plant Sciences, Research School of Biology, College of Medicine, Biology & Environment, The Australian National University, Canberra, ACT 0200, AustraliaDivision of Plant Sciences, Research School of Biology, College of Medicine, Biology & Environment, The Australian National University, Canberra, ACT 0200, AustraliaDivision of Plant Sciences, Research School of Biology, College of Medicine, Biology & Environment, The Australian National University, Canberra, ACT 0200, AustraliaMicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require “read count” to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA∗ duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.http://dx.doi.org/10.1155/2012/652979 |
| spellingShingle | Philip H. Williams Rod Eyles Georg Weiller Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees Journal of Nucleic Acids |
| title | Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees |
| title_full | Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees |
| title_fullStr | Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees |
| title_full_unstemmed | Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees |
| title_short | Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees |
| title_sort | plant microrna prediction by supervised machine learning using c5 0 decision trees |
| url | http://dx.doi.org/10.1155/2012/652979 |
| work_keys_str_mv | AT philiphwilliams plantmicrornapredictionbysupervisedmachinelearningusingc50decisiontrees AT rodeyles plantmicrornapredictionbysupervisedmachinelearningusingc50decisiontrees AT georgweiller plantmicrornapredictionbysupervisedmachinelearningusingc50decisiontrees |