Abstract
miRNA is a kind of single non-coding RNA that plays a pivotal regulated role in gene expression and has a very important influence in disease occurrence, growth and development, cell proliferation and so on. Therefore prediction miRNA has become the most important task in understanding miRNA regulation mechanism. Existing computational prediction methods are usually good at recognition pre-miRNA with multiple stem-loops. In this study, in order to further improve predictive precision of pre-miRNA, we quoted a set of new biologically multiple stem and loop secondary structure features based on the previous research work, then handled the imbalance problem of dataset, combined feedforward neural network. Finally, the new classifier system was constructed successfully with the proposed approach to separate the real pre-miRNA from datasest. By using the dataset of human pre-miRNAs and employing systematic 5-fold cross-validation methods for evaluating the classifier performance. We discover that the new classifier improved predictive precision effectively.
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Acknowledgement
Gaoqiang Yu and Dong Wang contributed equally to this work and should be considered co-first authors. This research was partially supported by Program for Scientific research innovation team in Colleges and universities of Shandong Province 2012–2015, the Key Project of Natural Science Foundation of Shandong Province (ZR2011FZ001), the Natural Science Foundation of Shandong Province (ZR2011FL022, ZR2013FL002), the Key Subject Research Foundation of Shandong Province and the Shandong Provincial Key Laboratory of Network Based Intelligent Computing. This work was also supported by the National Natural Science Foundation of China (Grant No. 61302128, 61201428, 61203105).The scientific research foundation of University of Jinan(xky1109).
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Yu, G., Wang, D., Chen, Y. (2015). Prediction of Pre-miRNA with Multiple Stem-Loops Using Feedforward Neural Network. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_55
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DOI: https://doi.org/10.1007/978-3-319-22186-1_55
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