{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T20:24:08Z","timestamp":1791836648823,"version":"4.3.4"},"reference-count":89,"publisher":"American Society for Microbiology","issue":"8","license":[{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["mSystems"],"published-print":{"date-parts":[[2024,8,20]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>Microbiome studies have revealed gut microbiota\u2019s potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn\u2019s disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson\u2019s disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer\u2019s disease vs CD and UC. These findings, detected by our pipeline, provide valuable insights into these diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>Assessing disease similarity is an essential initial step preceding a disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual diseases to understand their unique characteristics, which by design excludes comorbidities in individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilizes both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/msystems.00295-24","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T09:01:32Z","timestamp":1722330092000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":22,"title":["Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases"],"prefix":"10.1128","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1131-4801","authenticated-orcid":true,"given":"Dong-Min","family":"Jin","sequence":"first","affiliation":[{"name":"Center for Genomics and Systems Biology, New York University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James 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