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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Stable Gene Signature Selection for Prediction of Breast Cancer Recurrence Using Joint Mutual Information
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Stable Gene Signature Selection for Prediction of Breast Cancer Recurrence Using Joint Mutual Information

机译：使用联合共同信息预测乳腺癌复发的稳定基因签名选择

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摘要

In this experiment, a gene selection technique was proposed to select a robust gene signature from microarray data for prediction of breast cancer recurrence. In this regard, a hybrid scoring criterion was designed as linear combinations of the scores that were determined in the mutual information (MI) domain and protein-protein interactions network. Whereas, the MI-based score represents the complementary information between the selected genes for outcome prediction; and the number of connections in the PPI network between the selected genes builds the PPI-based score. All genes were scored by using the proposed function in a hybrid forward-backward gene-set selection process to select the optimum biomarker-set from the gene expression microarray data. The accuracy and stability of the finally selected biomarkers were evaluated by using five-fold cross-validation (CV) to classify available data on breast cancer patients into two cohorts of poor and good prognosis. The results showed an appealing improvement in the cross-dataset accuracy in comparison with similar studies whenever we applied a primary signature, which was selected from one dataset, to predict survival in other independent datasets. Moreover, the proposed method demonstrated 58-92 percent overlap between 50-genes signatures, which were selected from seven independent datasets individually.
机译：在该实验中，提出了一种基因选择技术，可以从微阵列数据中选择强大的基因特征来预测乳腺癌的复发。在这方面，将杂项评分标准设计为在互信息（MI）域和蛋白质-蛋白质相互作用网络中确定的分数的线性组合。鉴于基于MI的评分代表了用于预测结果的所选基因之间的互补信息； PPI网络中所选基因之间的连接数建立了基于PPI的评分。通过在混合向前-向后基因集选择过程中使用拟议的功能对所有基因进行评分，以从基因表达微阵列数据中选择最佳的生物标志物集。最终选择的生物标志物的准确性和稳定性通过使用五重交叉验证（CV）进行评估，以将乳腺癌患者的可用数据分为不良和良好预后的两个队列。结果表明，每当我们应用从一个数据集中选择的主要特征来预测其他独立数据集中的生存率时，与类似研究相比，跨数据集准确性都有了令人瞩目的提高。此外，所提出的方法证明了50个基因的签名之间有58-92％的重叠，这是分别从七个独立的数据集中选择的。

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