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MiRNATIP: a SOM-based miRNA-target interactions predictor

机译：MiRNATIP：基于SOM的miRNA-靶标相互作用预测子

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Background MicroRNAs (miRNAs) are small non-coding RNA sequences with regulatory functions to post-transcriptional level for several biological processes, such as cell disease progression and metastasis. MiRNAs interact with target messenger RNA (mRNA) genes by base pairing. Experimental identification of miRNA target is one of the major challenges in cancer biology because miRNAs can act as tumour suppressors or oncogenes by targeting different type of targets. The use of machine learning methods for the prediction of the target genes is considered a valid support to investigate miRNA functions and to guide related wet-lab experiments. In this paper we propose the miRNA Target Interaction Predictor (miRNATIP) algorithm, a Self-Organizing Map (SOM) based method for the miRNA target prediction. SOM is trained with the seed region of the miRNA sequences and then the mRNA sequences are projected into the SOM lattice in order to find putative interactions with miRNAs. These interactions will be filtered considering the remaining part of the miRNA sequences and estimating the free-energy necessary for duplex stability. Results We tested the proposed method by predicting the miRNA target interactions of both the Homo sapiens and the Caenorhbditis elegans species; then, taking into account validated target (positive) and non-target (negative) interactions, we compared our results with other target predictors, namely miRanda, PITA, PicTar, mirSOM, TargetScan and DIANA-microT, in terms of the most used statistical measures. We demonstrate that our method produces the greatest number of predictions with respect to the other ones, exhibiting good results for both species, reaching the for example the highest percentage of sensitivity of 31 and 30.5 % , respectively for Homo sapiens and for C. elegans . All the predicted interaction are freely available at the following url: http://tblab.pa.icar.cnr.it/public/miRNATIP/ . Conclusions Results state miRNATIP outperforms or is comparable to the other six state-of-the-art methods, in terms of validated target and non-target interactions, respectively.
机译：背景技术MicroRNA（miRNA）是小的非编码RNA序列，具有调控功能，可在多种生物学过程（如细胞疾病进展和转移）中达到转录后水平。 MiRNA通过碱基配对与目标信使RNA（mRNA）基因相互作用。 miRNA靶标的实验鉴定是癌症生物学中的主要挑战之一，因为miRNA可以通过靶向不同类型的靶标而充当肿瘤抑制物或致癌基因。使用机器学习方法预测目标基因被认为是研究miRNA功能和指导相关湿实验室实验的有效支持。在本文中，我们提出了miRNA目标相互作用预测器（miRNATIP）算法，这是一种基于自组织映射（SOM）的miRNA目标预测方法。用miRNA序列的种子区域训练SOM，然后将mRNA序列投影到SOM晶格中，以发现与miRNA的推定相互作用。考虑到miRNA序列的其余部分并估算双链体稳定性所需的自由能，将过滤掉这些相互作用。结果我们通过预测智人和秀丽隐杆线虫物种的miRNA靶相互作用来测试提出的方法。然后，考虑到已验证的目标（阳性）和非目标（阴性）相互作用，我们将我们的结果与其他目标预测变量，即miRanda，PITA，PicTar，mirSOM，TargetScan和DIANA-microT进行了比较，以最常用的统计数据进行比较措施。我们证明了我们的方法相对于其他方法产生的预测数量最多，对两个物种都显示出良好的结果，例如，对智人和秀丽隐杆线虫的敏感度百分比最高，分别达到31％和30.5％。可以从以下URL免费获得所有预测的交互：http://tblab.pa.icar.cnr.it/public/miRNATIP/。结论结果表明，就验证的靶标和非靶标相互作用而言，miRNATIP的性能优于或与其他六种最新方法相当。

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