ReviewCell systems2024
The structure is the message: Preserving experimental context through tensor decomposition.
Review in Cell systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Tensor-based integration of time-series measurements reveals relationships between underlying disease, early injury, and T cell polarization in liver transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2026Article
- Decoding Immune Regulation: From Genetic Variation to Mechanism Through Single-Cell Genomics.Immune network · 2026Review
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- Tensor-cell2cell v2 unravels coordinated dynamics of protein- and metabolite-mediated cell-cell communication.Bioinformatics (Oxford, England) · 2026Article
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
- Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE.Cell systems · 2025Article
- Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood.NPJ systems biology and applications · 2024Article
- Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "the medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.
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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.