On the performance of pre-micro{RNA} detection algorithms
| العنوان: | On the performance of pre-micro{RNA} detection algorithms |
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| المؤلفون: | Jens Allmer, Müşerref Duygu Saçar Demirci, Jan Baumbach |
| المساهمون: | TR107974, Saçar Demirci, Müşerref Duygu, Allmer, Jens, Izmir Institute of Technology. Molecular Biology and Genetics |
| المصدر: | Nature Communications, Vol 8, Iss 1, Pp 1-9 (2017) Saçar Demirci, M D, Baumbach, J & Allmer, J 2017, ' On the performance of pre-microRNA detection algorithms ', Nature Communications, vol. 8, 330 . https://doi.org/10.1038/s41467-017-00403-zTest Nature Communications |
| بيانات النشر: | Nature Publishing Group, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Computer science, Computation, Science, General Physics and Astronomy, Pre-MicroRNA, RNA precursor, Article, General Biochemistry, Genetics and Molecular Biology, Computational biology, Machine Learning, 03 medical and health sciences, Software, RNA Precursors, Humans, RNA Precursors/genetics, lcsh:Science, Multidisciplinary, business.industry, Computational Biology, Reproducibility of Results, General Chemistry, Computational Biology/methods, Ensemble learning, Data set, MicroRNAs, MicroRNAs/genetics, 030104 developmental biology, Gene Expression Regulation, lcsh:Q, business, Algorithm, Algorithms |
| الوصف: | MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes. As the experimental discovery of microRNAs (miRNAs) is cumbersome, computational tools have been developed for the prediction of pre-miRNAs. Here the authors develop a framework to assess the performance of existing and novel pre-miRNA prediction tools and provide guidelines for selecting an appropriate approach for a given data set. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2041-1723 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c0ebaddffbf9f29cb52ddb2f9d497b9Test http://link.springer.com/article/10.1038/s41467-017-00403-zTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....8c0ebaddffbf9f29cb52ddb2f9d497b9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20411723 |
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