  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.
