ArticlePLoS computational biology2022
Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- Maternal antibiotic prophylaxis during cesarean section has a limited impact on the infant gut microbiome.Cell host & microbe · 2024Trial
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- Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective.bioRxiv : the preprint server for biology · 2026Article
- Extracting host-specific developmental signatures from longitudinal microbiome data.PLoS computational biology · 2026Article
- Quantitative susceptibility mapping demonstrates ageing-related long-term structural brain changes in adult rats after mild ischaemic stroke.Brain communications · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- LorDist: a novel method for calculating the distance based on functional data analysis with application to longitudinal microbial data.Microbiology spectrum · 2025Article
- Targeting CD38 immunometabolic checkpoint improves metabolic fitness and cognition in a mouse model of Alzheimer's disease.Nature communications · 2025Article
- Recent advances in precision nutrition and cardiometabolic diseases.Revista espanola de cardiologia (English ed.) · 2025Review
- TEMPTED: time-informed dimensionality reduction for longitudinal microbiome studies.Genome biology · 2024Article
- Bioinformatics approaches for studying molecular sex differences in complex diseases.Briefings in bioinformatics · 2024Review
- The structure is the message: Preserving experimental context through tensor decomposition.Cell systems · 2024Review
- Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease.NPJ systems biology and applications · 2024Article
- MeTEor: an R Shiny app for exploring longitudinal metabolomics data.Bioinformatics advances · 2024Article
- Stable tensor neural networks for efficient deep learning.Frontiers in big data · 2024Article
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
- A Perspective: Challenges in Dementia Research.Medicina (Kaunas, Lithuania) · 2022Article
- Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype.mSystems · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Longitudinal 'omics analytical methods are extensively used in the evolving field of precision medicine, by enabling 'big data' recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbations such as disease pathogenesis, medical treatment or changes in lifestyle. However, inherent technical limitations in biomedical studies often result in the generation of feature-rich and sample-limited datasets. Analyzing such data using conventional modalities often proves to be challenging since the repeated, high-dimensional measurements overload the outlook with inconsequential variations that must be filtered from the data in order to find the true, biologically relevant signal. Tensor methods for the analysis and meaningful representation of multiway data may prove useful to the biological research community by their advertised ability to tackle this challenge. In this study, we present tcam-a new unsupervised tensor factorization method for the analysis of multiway data. Building on top of cutting-edge developments in the field of tensor-tensor algebra, we characterize the unique mathematical properties of our method, namely, 1) preservation of geometric and statistical traits of the data, which enable uncovering information beyond the inter-individual variation that often takes over the focus, especially in human studies. 2) Natural and straightforward out-of-sample extension, making tcam amenable for integration in machine learning workflows. A series of re-analyses of real-world, human experimental datasets showcase these theoretical properties, while providing empirical confirmation of tcam's utility in the analysis of longitudinal 'omics data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.