ArticleMolecular systems biology2021
Tensor-structured decomposition improves systems serology analysis.
Article in Molecular systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Assessing tensor decomposition quality of immune profiling data from a dictionary learning perspective.bioRxiv : the preprint server for biology · 2026Article
- Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE.Cell systems · 2025Article
- A multivalent binding model infers antibody Fc species from systems serology.PLoS computational biology · 2024Article
- Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood.NPJ systems biology and applications · 2024Article
- The structure is the message: Preserving experimental context through tensor decomposition.Cell systems · 2024Review
- Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization.bioRxiv : the preprint server for biology · 2024Article
- Tensor modeling of MRSA bacteremia cytokine and transcriptional patterns reveals coordinated, outcome-associated immunological programs.PNAS nexus · 2024Article
- Synthetic living materials in cancer biology.Nature reviews bioengineering · 2023Article
- Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood.bioRxiv : the preprint server for biology · 2023Article
- Multivalent, asymmetric IL-2-Fc fusions show enhanced selectivity for regulatory T cells.Science signaling · 2023Article
- Tensor-based insights into systems immunity and infectious disease.Trends in immunology · 2023Article
- Article
- Systems approaches to uncovering the contribution of environment-mediated drug resistance.Current opinion in solid state & materials science · 2022Article
- Tensor-structured decomposition improves systems serology analysis.Molecular systems biology · 2021Article
- Quantitatively Visualizing Bipartite Datasets.Physical review. XArticle
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Authors and funding
5 authors.
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Abstract
Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.
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