Knowledge of the sub-cellular localization of the most diverse class of transcribed RNA, long non-coding RNAs (lncRNAs) will lead us to identify different types of cancers and other diseases as lncRNAs play key role in related cellular functions. In recent days with the exponential growth of known records, it becomes essential to establish new machine learning based techniques to identify the new one due to faster and cheaper solutions provided compared to laboratory methods. In this paper, we propose Locate-R, a novel method for predicting the sub-cellular location of lncRNAs. We have used only n-gapped l-mer composition and l-mer composition as features and select best 655 features to build the model. This model is based locally deep support vector machines which significantly enhance the prediction accuracy with respect to exiting state-of-the-art methods. Our predictor is readily available for use as a stand-alone web application from: http://locate-r.azurewebsites.net/.

中文翻译：

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Locate-R：使用核苷酸组成的长非编码RNA的亚细胞定位。

较长的非编码RNA（lncRNA）是最广泛的转录RNA类型的亚细胞定位知识，随着lncRNA在相关细胞功能中发挥关键作用，将使我们识别出不同类型的癌症和其他疾病。近年来，随着已知记录的呈指数增长，由于提供了比实验室方法更快，更便宜的解决方案，因此建立基于新机器学习的技术来识别新技术变得至关重要。在本文中，我们提出了Locate-R，这是一种预测lncRNAs亚细胞定位的新方法。我们仅使用n缺口的L-mer成分和L-mer成分作为特征，并选择最佳655个特征来构建模型。该模型基于局部深度支持向量机，相对于现有的现有方法，该机器可以大大提高预测精度。我们的预测变量可随时从以下网站用作独立的Web应用程序：http://locate-r.azurewebsites.net/。