{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:36:29Z","timestamp":1790094989265,"version":"4.0.1"},"reference-count":60,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acad Dermatol Venereol"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or require skilled expertise, warranting alternate tests. Application of machine\u2010learning tools to heterogeneous and high\u2010dimensional multi\u2010omics datasets finds promising potential in data integration and may aid biomarker discovery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The present study aimed to evaluate the potential of the microbiome and metabolome\u2010derived biomarkers in BV diagnosis. Interpretable machine\u2010learning algorithms were used to evaluate the utility of an integrated\u2010omics\u2010derived classification model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Vaginal samples obtained from reproductive\u2010age group women with (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200940) and without BV (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200940) were subjected to 16S rRNA amplicon sequencing and LC\u2013MS\u2010based metabolomics. The vaginal microbiome and metabolome were characterized, and machine\u2010learning analysis was performed to build a classification model using biomarkers with the highest diagnostic accuracy.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Microbiome\u2010based diagnostic model exhibited a ROC\u2010AUC (10\u2010fold CV) of 0.84\u2009\u00b1\u20090.21 and accuracy of 0.79\u2009\u00b1\u20090.18, and important features were\n                      <jats:italic>Aerococcus<\/jats:italic>\n                      spp.,\n                      <jats:italic>Mycoplasma hominis<\/jats:italic>\n                      ,\n                      <jats:italic>Sneathia<\/jats:italic>\n                      spp.,\n                      <jats:italic>Lactobacillus<\/jats:italic>\n                      spp.,\n                      <jats:italic>Prevotella<\/jats:italic>\n                      spp.,\n                      <jats:italic>Gardnerella<\/jats:italic>\n                      spp. and\n                      <jats:italic>Fannyhessea vaginae<\/jats:italic>\n                      . The metabolome\u2010derived model displayed superior performance with a ROC\u2010AUC of 0.97\u2009\u00b1\u20090.07 and an accuracy of 0.92\u2009\u00b1\u20090.08. Beta\u2010leucine, methylimidazole acetaldehyde, dimethylethanolamine, L\u2010arginine and beta cortol were among key predictive metabolites for BV. A predictive model combining both microbial and metabolite features exhibited a high ROC\u2010AUC of 0.97\u2009\u00b1\u20090.07 and accuracy of 0.94\u2009\u00b1\u20090.08 with diagnostic performance only slightly superior to the metabolite\u2010based model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Application of machine\u2010learning tools to multi\u2010omics datasets aid biomarker discovery with high predictive performance. Metabolome\u2010derived classification models were observed to have superior diagnostic performance in predicting BV than microbiome\u2010based biomarkers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jdv.19805","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T04:31:04Z","timestamp":1706502664000},"page":"1152-1165","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Multi\u2010omics analysis identifies potential microbial and metabolite diagnostic biomarkers of bacterial vaginosis"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8886-6460","authenticated-orcid":false,"given":"A.","family":"Challa","sequence":"first","affiliation":[{"name":"Department of Dermatology and Venereology All India Institute of Medical Sciences  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. 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