ArticleFrontiers in microbiology2022
Causality and correlation analysis for deciphering the microbial interactions in activated sludge.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Culture-Based Wastewater Surveillance for the Detection and Monitoring of Antimicrobial Resistance in Staphylococcal Species.Veterinary sciences · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Time series data has been considered to be a massive information provider for comprehending more about microbial dynamics and interaction, leading to a causality inference in a complex microbial community. Granger causality and correlation analysis have been investigated and applied for the construction of a microbial causal correlation network (MCCN) and efficient prediction of the ecological interaction within activated sludge, which thereby exhibited ecological interactions at the OTU-level. Application of MCCN to a time series of activated sludge data revealed that the hub species OTU56, classified as the one belonging to the genus
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Registered trials
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