{"doi":"10.1080/19490976.2023.2205386","title":"Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers","abstract":"Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.","journal":"Gut Microbes","year":2023,"id":10005,"datarank":0.5570358100056463,"base_score":3.713572066704308,"endowment":3.713572066704308,"self_citation_contribution":0.5570358100056463,"citation_network_contribution":0.0,"self_endowment_contribution":0.5570358100056463,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0575,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-05-04","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":58054,"name":"Jinxin Liu","orcid":"0000-0003-0753-5342","position":1,"is_corresponding":false},{"id":58052,"name":"Jiaying Zhu","orcid":"0000-0001-5247-7829","position":2,"is_corresponding":false},{"id":85491,"name":"Huarui Wang","orcid":null,"position":3,"is_corresponding":false},{"id":39634,"name":"Chuqing Sun","orcid":"0000-0001-5025-2650","position":4,"is_corresponding":false},{"id":31693,"name":"Na L Gao","orcid":null,"position":5,"is_corresponding":false},{"id":23546,"name":"Wei-Hua Chen","orcid":"0000-0001-5160-4398","position":7,"is_corresponding":false},{"id":31699,"name":"Na Gao","orcid":"0000-0002-9095-0987","position":8,"is_corresponding":false},{"id":23552,"name":"Xing‐Ming Zhao","orcid":"0000-0002-4531-3970","position":9,"is_corresponding":false},{"id":58053,"name":"Min Li","orcid":"0000-0002-0047-2804","position":0,"is_corresponding":true}],"reference_count":129,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}