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Precision medicine is a clinical approach for disease prevention, detectionand treatment, which considers each individual's genetic background,environment and lifestyle. The development of this tailored avenue has beendriven by the increased availability of omics methods, large cohorts oftemporal samples, and their integration with clinical data. Despite the immenseprogression, existing computational methods for data analysis fail to provideappropriate solutions for this complex, high-dimensional and longitudinal data.In this work we have developed a new method termed TCAM, a dimensionalityreduction technique for multi-way data, that overcomes major limitations whendoing trajectory analysis of longitudinal omics data. Using real-world data, weshow that TCAM outperforms traditional methods, as well as state-of-the-arttensor-based approaches for longitudinal microbiome data analysis. Moreover, wedemonstrate the versatility of TCAM by applying it to several different omicsdatasets, and the applicability of it as a drop-in replacement withinstraightforward ML tasks.

Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization