Long Non-coding RNA Based Cancer Classification using Deep Neural Networks
Long Non-coding RNA Based Cancer Classification using Deep Neural Networks
复制标题
使用深度神经网络进行基于长非编码 RNA 的癌症分类
DOI：
10.1145/3307339.3343249
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Mondal, Ananda M.
中科院分区：
文献类型：
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作者：
Mamun, Abdullah A.;Mondal, Ananda M.
最近的研究表明，lncRNA在肿瘤发生中起着关键作用，lncRNA的错误表达可导致参与癌症进展不同方面的各种靶基因表达谱的改变。然而，仅使用lncRNA对多种癌症类型进行分类的研究很少。在本文中，我们通过使用四种深度神经网络-多层感知器（MLP），长短期记忆（LSTM），卷积神经网络（CNN）和深度自动编码器（DAE）来探索lncRNA在癌症类型分类中的能力。对于实验，使用来自8种癌症- BLCA、CESC、COAD、HNSC、KIRP、LGG、LIHC和LUAD -的TCGA的RNA-seq表达值。组合数据集由3656名患者组成，具有12309个lncRNA的表达值。模型在准确度方面的性能范围为94%至98%，这表明lncRNA表达谱在分类癌症类型时与mRNA表达谱相比是更好的特征。
Recent studies indicate that lncRNA plays key roles in tumorigenesis and misexpression of lncRNAs can lead to change in expression profiles of various target genes involved in different aspects of cancer progression. However, research on classifying multiple cancer types using only lncRNA is rarely found. In this paper, we explored the capability of lncRNA in classifying cancer types by employing four deep neural networks - multi-layer perceptron (MLP), longshort- term memory (LSTM), convolutional neural network (CNN) and deep autoencoder (DAE). For experiment, RNA-seq expression values from TCGA for 8 cancers - BLCA, CESC, COAD, HNSC, KIRP, LGG, LIHC, and LUAD - are used. The combined dataset consists of 3656 patients with expression values for 12309 lncRNAs. The performance of the models in terms of accuracy ranges from 94% to 98%, which shows lncRNA expression profiles as the better signature compared to the mRNA expression profiles in classifying cancer types.