A Distributed Classifier for MicroRNA Target Prediction with Validation Through TCGA Expression Data.
A Distributed Classifier for MicroRNA Target Prediction with Validation Through TCGA Expression Data.
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DOI：
10.1109/tcbb.2018.2828305
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Chaterji S
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作者：
Ghoshal A;Zhang J;Roth MA;Xia KM;Grama AY;Chaterji S
microRNA（miRNAs）是一种长度约为22个核苷酸的调控RNA，通过与同源mRNA靶区结合来介导RNA干扰。在这里，我们提出了一个分布式核支持向量机的二进制分类方案来预测miRNA的目标。它通过光滑的B样条曲线捕获miRNA-mRNA相互作用的空间分布。这对于各种输入特征（例如热力学和基于序列的特征）是单独完成的。此外，我们使用一种原则性的方法来统一建模规范和非规范的种子匹配，使用一种新的种子富集度量。最后，我们使用基于弹性网络的回归模型对四种癌症类型的TCGA表达数据进行验证，以估计共同调节任何给定mRNA的miRNA。我们提出了一套用于miRNA靶点预测的算法，在Avishkar的旗帜下，具有上级预测性能。具体来说，我们最终的核SVM模型，与Apache Spark后端，实现了平均真阳性率（TPR）超过75%，当保持20%的假阳性率，非典型的人类miRNA靶位点。这是一个超过150%的改进TPR的非典型网站，在同类最佳算法。我们能够实现这样的上级性能，通过代表的热力学和序列的曲线，设计一种新的种子富集度量，并学习一个合奏的miRNA家族特异性核SVM分类器的miRNA-mRNA相互作用。我们提供了一个易于使用的系统，用于大规模的交互式分析和预测miRNA靶点。我们系统中的所有操作，即候选集生成，特征生成和转换，训练，预测和计算性能指标都是完全分布式的，并且是可扩展的。我们已经开发了一种有效的基于SVM的模型，用于使用最近的CLIP-seq数据进行miRNA靶标预测，使用不同物种（人或小鼠）或不同靶标类型（典型或非典型）的ROC曲线进行评估，显示出上级性能。我们使用CLIP-seq数据和来自四种癌症类型的表达数据分析了靶配对之间的一致性。据我们所知，我们提供了第一个基于Apache Hadoop和Spark的分布式miRNA靶点预测框架。所有源代码和示例数据都可以在https://bitbucket.org/cellsandmachines/avishkar上公开获得。我们使用Apache Spark实现的可扩展内核SVM可用于解决大规模非线性二进制分类问题，可在https://bitbucket.org/cellsandmachines/kernelsvmspark上获得。
MicroRNAs (miRNAs) are approximately 22-nucleotide long regulatory RNA that mediate RNA interference by binding to cognate mRNA target regions. Here, we present a distributed kernel SVM-based binary classification scheme to predict miRNA targets. It captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves. This is accomplished separately for various input features, such as thermodynamic and sequence-based features. Further, we use a principled approach to uniformly model both canonical and non-canonical seed matches, using a novel seed enrichment metric. Finally, we verify our miRNA-mRNA pairings using an Elastic Net-based regression model on TCGA expression data for four cancer types to estimate the miRNAs that together regulate any given mRNA. We present a suite of algorithms for miRNA target prediction, under the banner Avishkar, with superior prediction performance over the competition. Specifically, our final kernel SVM model, with an Apache Spark backend, achieves an average true positive rate (TPR) of more than 75%, when keeping the false positive rate of 20%, for non-canonical human miRNA target sites. This is an improvement of over 150% in the TPR for non-canonical sites, over the best-in-class algorithm. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of miRNA-mRNA interaction as curves, devising a novel seed enrichment metric, and learning an ensemble of miRNA family-specific kernel SVM classifiers. We provide an easy-to-use system for large-scale interactive analysis and prediction of miRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction and computing performance metrics are fully distributed and are scalable. We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves for different species (human or mouse), or different target types (canonical or non-canonical). We analyzed the agreement between the target pairings using CLIP-seq data and using expression data from four cancer types. To the best of our knowledge, we provide the first distributed framework for miRNA target prediction based on Apache Hadoop and Spark. All source code and sample data are publicly available at https://bitbucket.org/cellsandmachines/avishkar. Our scalable implementation of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems, is available at https://bitbucket.org/cellsandmachines/kernelsvmspark.