论文标题
从DNA突变（MUT2EX）重建基因表达和敲除效应评分：方法和应用癌症预测问题
Reconstructing gene expression and knockout effect scores from DNA mutation (Mut2Ex): methodology and application to cancer prediction problems
论文作者
论文摘要
当只有有限的突变特征可用时，建立临床相关结果的预测模型会导致巨大的挑战，这是由于数据的稀疏性和低差异性。在本文中，我们提出了一种通过利用个人突变谱与其相应基因表达或敲除效应谱之间的多模式关联关系来增强这些特征的预测能力的方法。因此，我们可以从可用的突变特征重建感兴趣的基因的表达或效应评分，然后直接使用此重建表示形式来建模和预测临床结果。我们表明，与仅利用原始突变数据的模型相比，我们的方法在预测准确性方面产生显着提高，并得出与使用真实表达或效应曲线获得的结论相当的结论。
Building prediction models for outcomes of clinical relevance when only a limited number of mutational features are available causes considerable challenges due to the sparseness and low-dimensionality of the data. In this article, we present a method to augment the predictive power of these features by leveraging multi-modal associative relationships between an individual's mutational profile and their corresponding gene expression or knockout effect profiles. We can thus reconstruct expression or effect scores for genes of interest from the available mutation features and then use this reconstructed representation directly to model and predict clinical outcomes. We show that our method produces significant improvements in predictive accuracy compared to models utilizing only the raw mutational data, and results in conclusions comparable to those obtained using real expression or effect profiles.
