Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree
MicroRNAs (miRNAs) are small, non‐coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully unders...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | FEBS Open Bio |
| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.fob.2015.10.003 |
| _version_ | 1811186203794341888 |
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| author | Behzad Rabiee-Ghahfarrokhi Fariba Rafiei Ali Akbar Niknafs Behzad Zamani |
| author_facet | Behzad Rabiee-Ghahfarrokhi Fariba Rafiei Ali Akbar Niknafs Behzad Zamani |
| author_sort | Behzad Rabiee-Ghahfarrokhi |
| collection | DOAJ |
| description | MicroRNAs (miRNAs) are small, non‐coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine‐learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time‐consuming experiments to achieve miRNA targets experimentally. Among the most important machine‐learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules. |
| first_indexed | 2024-04-11T13:42:43Z |
| format | Article |
| id | doaj.art-3e404f6d0e8346c181a61d1e70099c00 |
| institution | Directory Open Access Journal |
| issn | 2211-5463 |
| language | English |
| last_indexed | 2024-04-11T13:42:43Z |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | FEBS Open Bio |
| spelling | doaj.art-3e404f6d0e8346c181a61d1e70099c002022-12-22T04:21:12ZengWileyFEBS Open Bio2211-54632015-01-015187788410.1016/j.fob.2015.10.003Prediction of microRNA target genes using an efficient genetic algorithm‐based decision treeBehzad Rabiee-Ghahfarrokhi0Fariba Rafiei1Ali Akbar Niknafs2Behzad Zamani3Department of Information Technology, Kerman Graduate University of Advanced Technology, Kerman, IranDepartment of Plant Breeding and Biotechnology, Shahrekord University, Shahrekord, IranDepartment of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Engineering, Iran University of Science & Technology, Tehran, IranMicroRNAs (miRNAs) are small, non‐coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine‐learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time‐consuming experiments to achieve miRNA targets experimentally. Among the most important machine‐learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.https://doi.org/10.1016/j.fob.2015.10.003MicroRNA target predictionF-measureC4.5 decision treeClassification rulesGenetic algorithm |
| spellingShingle | Behzad Rabiee-Ghahfarrokhi Fariba Rafiei Ali Akbar Niknafs Behzad Zamani Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree FEBS Open Bio MicroRNA target prediction F-measure C4.5 decision tree Classification rules Genetic algorithm |
| title | Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree |
| title_full | Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree |
| title_fullStr | Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree |
| title_full_unstemmed | Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree |
| title_short | Prediction of microRNA target genes using an efficient genetic algorithm‐based decision tree |
| title_sort | prediction of microrna target genes using an efficient genetic algorithm based decision tree |
| topic | MicroRNA target prediction F-measure C4.5 decision tree Classification rules Genetic algorithm |
| url | https://doi.org/10.1016/j.fob.2015.10.003 |
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