MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one.

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References in zbMATH (referenced in 5 articles )

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  1. Chuang, Mao-Te; Hu, Ya-Han; Lo, Chia-Lun: Predicting the prolonged length of stay of general surgery patients: a supervised learning approach (2018)
  2. Rodríguez-Ezpeleta, Naiara (ed.); Hackenberg, Michael (ed.); Aransay, Ana M. (ed.): Bioinformatics for high throughput sequencing (2012)
  3. Wang, Minghao; Song, Xiaofeng; Han, Ping; Li, Wei; Jiang, Bin: New syntax to describe local continuous structure-sequence information for recognizing new pre-miRNAs (2010)
  4. Brameier, Markus; Wiuf, Carsten: Ab initio identification of human micrornas based on structure motifs (2007) ioport
  5. Jiang, Peng; Wu, Haonan; Wang, Wenkai; Ma, Wei; Sun, Xiao; Lu, Zuhong: Mipred: Classification of real and pseudo microrna precursors using random forest prediction model with combined features. (2007) ioport