PDF(Pubmed)
Abstract：
We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.
摘要：
我们提出了一种整合全基因组多组数据的创新策略，它通过利用多任务编码器从高维组学数据中导出的隐藏层特征来促进自适应合并。对八个基准癌症数据集的经验评估证实，我们提出的框架超过了癌症亚型的比较算法，提供优越的亚型结果。在这些子类型结果的基础上，我们建立了一个强大的管道来识别全基因组生物标志物，发掘195个重要的生物标志物。此外，我们进行了详尽的分析,以评估在癌症亚型分型过程中,在全基因组水平上每个组学和非编码区特征的重要性.我们的研究表明，组学和非编码区特征都会对癌症的发展和生存预后产生重大影响。这项研究强调了整合全基因组数据在癌症研究中的潜在和实际意义。证明了全面基因组表征的效力。此外,我们的发现为采用深度学习方法的多组学分析提供了有见地的观点.
