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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network
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iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network

机译：ILNCRNADIS-FB：通过深神经网络融合生物学特征块来鉴定LNCRNA疾病关联

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摘要

Identification of lncRNA-disease associations is not only important for exploring the disease mechanism, but will also facilitate the molecular targeting drug discovery. Fusing multiple biological information is able to generate a more comprehensive view of lncRNA-disease association feature. However, the existing fusion strategies in this field fail to remove the noisy and irrelevant information from each data source. As a result, their predictive performance is still too low to be applied to real world applications. In this regard, a novel computational predictor called iLncRNAdis-FB is proposed based on the Convolution Neural Network (CNN) to integrate different data sources by using the feature blocks in a supervised manner. The lncRNA similarity matrix and disease similarity matrix are constructed, based on which the three-dimensional feature blocks are generated. These feature blocks are then fed into CNN to train the model so as to predict unknown lncRNA-disease associations. Experimental results show that iLncRNAdis-FB achieves better performance compared with other state-of-the-art predictors. Furthermore, a web server of iLncRNAdis-FB has been established at http://bliulab.net/iLncRNAdis-FB/, by which users can submit lncRNA sequences to detect their potential associated diseases.
机译：鉴定LNCRNA疾病关联不仅重要的是探索疾病机制，而且还将促进分子靶向药物发现。融合多种生物信息能够产生更全面的LNCRNA疾病关联特征。但是，此字段中的现有融合策略未能从每个数据源中删除噪声和无关信息。因此，他们的预测性能仍然太低，无法应用于现实世界的应用程序。在这方面，基于卷积神经网络（CNN）提出了一种名为ILNCRNADIS-FB的新颖计算预测器，以通过以监督方式使用特征块来集成不同的数据源。基于该rNCRNA相似性矩阵和疾病相似性矩阵构成了三维特征块。然后将这些特征块送入CNN以训练模型，以预测未知的LNCRNA疾病关联。实验结果表明，与其他最先进的预测器相比，ILNCRNADIS-FB实现了更好的性能。此外，已经在http://bliulab.net //ilncrnadis-fb/处建立了ILNCrnadis-FB的Web服务器，用户可以提交LNCRNA序列以检测其潜在的相关疾病。

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