ArticlemSystems2025
parafac4microbiome: exploratory analysis of longitudinal microbiome data using parallel factor analysis.
Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Revealing subject-specific temporal patterns from longitudinal data.Bioinformatics (Oxford, England) · 2026Article
- Extracting host-specific developmental signatures from longitudinal microbiome data.PLoS computational biology · 2026Article
- Shifting hormones, shaping health: Gender-affirming therapy & oral care.International journal of transgender health · 2026Article
- ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation.PloS one · 2026Article
- Modelling of the Systemic Inflammatory Host Response in Relation to the Microbiome of the Endodontic Infection.International endodontic journal · 2025Article
- Multi-way modelling of oral microbial dynamics and host-microbiome interactions during induced gingivitis.NPJ biofilms and microbiomes · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Studies investigating microbial temporal dynamics are increasingly common, leveraging longitudinal designs that collect microbial abundance data across multiple time points from the same subjects. Traditional exploratory approaches like principal component analysis fail to fully utilize this structure. By organizing data as a three-way array-subjects as rows, microbial abundances as columns, and time points as the third dimension-multi-way methods such as parallel factor analysis (PARAFAC) can better capture temporal and structural patterns. This study demonstrates PARAFAC as a method to explore longitudinal microbiome data using three exemplary studies. In the first example, a long-time series of
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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.