Precision medicine is a clinical approach for disease prevention, detection and treatment, which considers each individual's genetic background, environment and lifestyle. The development of this tailored avenue has been driven by the increased availability of omics methods, large cohorts of temporal samples, and their integration with clinical data. Despite the immense progression, existing computational methods for data analysis fail to provide appropriate solutions for this complex, high-dimensional and longitudinal data. In this work we have developed a new method termed TCAM, a dimensionality reduction technique for multi-way data, that overcomes major limitations when doing trajectory analysis of longitudinal omics data. Using real-world data, we show that TCAM outperforms traditional methods, as well as state-of-the-art tensor-based approaches for longitudinal microbiome data analysis. Moreover, we demonstrate the versatility of TCAM by applying it to several different omics datasets, and the applicability of it as a drop-in replacement within straightforward ML tasks.
翻译:精密医学是预防、检测和治疗疾病的一种临床方法,它考虑到每个人的遗传背景、环境和生活方式。这一特制途径的开发,是由日益容易获得的动脉方法、大量时间样本及其与临床数据的结合所驱动的。尽管取得了巨大的进步,但现有的数据分析计算方法未能为这一复杂、高维度和纵向数据提供适当的解决办法。在这项工作中,我们开发了一种名为TCAM的新方法,即多路数据的维度减少技术,它克服了在对长视粒子数据进行轨迹分析时遇到的重大限制。我们利用现实世界数据,表明TCAM优于传统方法,以及以最新高压法为基础的长视微生物数据分析方法。此外,我们通过将它应用于若干不同的基因数据集,并把它作为直接的ML任务中的低位替代方法,来证明TCAM的多功能性。