{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sankaran K"],"funding":["Faculty of Science at McMaster University","National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["e1012196"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11210883"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(6)"],"pubmed_abstract":["Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies"],"journal":["PLoS computational biology"],"pubmed_title":["mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics."],"pmcid":["PMC11210883"],"funding_grant_id":["R01GM152744","R01 GM152744","20016699"],"pubmed_authors":["Sankaran K","Jeganathan P"],"additional_accession":[]},"is_claimable":false,"name":"mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.","description":"Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2025-04-19T18:32:14.751Z","creation":"2025-04-19T18:32:14.751Z"},"accession":"S-EPMC11210883","cross_references":{"pubmed":["38875277"],"doi":["10.1371/journal.pcbi.1012196"]}}