{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wu L"],"funding":["NICHD NIH HHS","NIA NIH HHS","NIAID NIH HHS","NHLBI NIH HHS","National Natural Science Foundation of China","NIH HHS"],"pagination":["2406"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10944475"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experim"],"journal":["Nature communications"],"pubmed_title":["Data-driven prediction of colonization outcomes for complex microbial communities."],"pmcid":["PMC10944475"],"funding_grant_id":["RF1 AG067744","R01 HD093761","U01 HL089856","K25 HL166208","R01 AI141529","U19 AI095219","UH3 OD023268","31971513"],"pubmed_authors":["Zuo W","Tao Z","Wu L","Dai L","Liu YY","Wang T","Zeng Y","Wang XW"],"additional_accession":[]},"is_claimable":false,"name":"Data-driven prediction of colonization outcomes for complex microbial communities.","description":"Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experim","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-07-16T04:54:34.822Z","creation":"2025-04-20T02:51:06.57Z"},"accession":"S-EPMC10944475","cross_references":{"pubmed":["38493186"],"doi":["10.1038/s41467-024-46766-y"]}}