Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, USA.
Department of Information and Statistics and Department of Bio&Medical Bigdata (BK21 Four program), Gyeongsang National University, Jinju, Republic of Korea.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae239.
Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.
大多数常见疾病在患病率、严重程度和遗传易感性方面存在性别差异。然而，大多数遗传和临床结果研究都是在考虑性别的综合框架中设计的，将性别视为一个协变量。少数专门针对男性和女性的研究分别进行了分析，未能确定基因与性别的相互作用。在这里，我们提出了一种新的基于深度学习的统一生物学可解释框架（命名为 SPIN），用于分析性别差异。我们证明，SPIN 显著提高了 TCGA 癌症数据集的 C 指数高达 23.6%，并使用哮喘数据集进行了进一步验证。此外，SPIN 确定了性别特异性和共享风险位点，这些通常在以前的性别综合/分离分析中被忽略。我们还表明，SPIN 可以解释生物途径如何导致性别差异，并在个体水平上提高风险预测，这可能导致针对特定个体特征的精准医学的发展。
