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首页> 外文会议>International work-conference on bioinformatics and biomedical engineering >Identification of Common Gene Signatures in Microarray and RNA-Sequencing Data Using Network-Based Regularization
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Identification of Common Gene Signatures in Microarray and RNA-Sequencing Data Using Network-Based Regularization

机译：使用基于网络的正则化方法鉴定微阵列和RNA测序数据中的常见基因签名

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Microarray and RNA-sequencing (RNA-seq) gene expression data alongside machine learning algorithms are promising in the discovery of new cancer biomarkers. However, even though they are similar in purpose, there are some fundamental differences between the two techniques. We propose a methodology for cross-platform integration, and biomarker discovery based on network-based regularization via the Twin Networks Recovery (twiner) penalty, as a strategy to enhance the selection of breast cancer gene signatures that have similar correlation patterns in both platforms. In a classification setting based on sparse logistic regression (LR) taking as classes tumor from both RNA-seq and microarray, and normal tissue samples, twiner achieved precision-recall accuracies of 99.71% and 99.57% in the training and test set, respectively. Moreover, the survival analysis results validated the biological relevance of the signatures identified by twiner. Therefore, by leveraging from the existing amount of data for microarray and RNA-seq, a single biological conclusion can be reached, independent of each technology.
机译：微阵列和RNA测序（RNA-seq）基因表达数据以及机器学习算法在发现新的癌症生物标志物方面很有前途。但是，即使它们的目的相似，但两种技术之间还是存在一些根本的区别。我们提出了一种跨平台整合的方法，以及基于基于双胞胎网络恢复（双胞胎）惩罚的基于网络的正则化的生物标记物发现的方法，以此作为一种策略来增强两个平台中具有相似相关模式的乳腺癌基因签名的选择。在基于稀疏逻辑回归（LR）的分类设置中，以RNA-seq和微阵列中的肿瘤以及正常组织样品为分类，twiner在训练和测试集中分别达到了99.71％和99.57％的精确召回率。此外，生存分析结果验证了Twiner鉴定出的特征标记的生物学相关性。因此，通过利用微阵列和RNA-seq的现有数据量，可以得出单个生物学结论，而与每种技术无关。

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