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MiRANN: A reliable approach for improved classification of precursor microRNA using Artificial Neural Network model

机译：MiRANN：使用人工神经网络模型改进前体microRNA分类的可靠方法

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摘要

MicroRNA (miRNA) is a special class of short noncoding RNA that serves pivotal function of regulating gene expression. The computational prediction of new miRNA candidates involves various methods such as learning methods and methods using expression data. This article has proposed a reliable model - miRANN which is a supervised machine learning approach. MiRANN used known pre-miRNAs as positive set and a novel negative set from human CDS regions. The number of known miRNAs is now huge and diversified that could cover almost all characteristics of unknown miRNAs which increases the quality of the result (99.9% accuracy, 99.8% sensitivity, 100% specificity) and provides a more reliable prediction. MiRANN performs better than other state-of-the-art approaches and declares to be the most potential tool to predict novel miRNAs. We have also tested our result using a previous negative set. MiRANN, opens new ground using ANN for predicting pre-miRNAs with a promise of better performance.
机译：MicroRNA（miRNA）是一类特殊的短非编码RNA，具有调节基因表达的关键功能。新miRNA候选物的计算预测涉及多种方法，例如学习方法和使用表达数据的方法。本文提出了一种可靠的模型-miRANN，这是一种受监督的机器学习方法。 MiRANN使用已知的pre-miRNA作为人类CDS区的阳性集和新型阴性集。现在，已知的miRNA数量众多且种类繁多，可以涵盖未知miRNA的几乎所有特征，从而提高了结果的质量（99.9％的准确度，99.8％的灵敏度，100％的特异性）并提供了更可靠的预测。 MiRANN的性能优于其他最新方法，并且被宣布为预测新型miRNA的最有力工具。我们还使用先前的负数集测试了我们的结果。 MiRANN使用ANN预测pre-miRNA并有望提供更好的性能开辟了新天地。

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