{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:33:50Z","timestamp":1783438430472,"version":"3.54.6"},"reference-count":81,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073231"],"award-info":[{"award-number":["62073231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176175"],"award-info":[{"award-number":["62176175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372318"],"award-info":[{"award-number":["62372318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe\u2013disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe\u2013disease association matrix. Our CMFHMDA model is subsequently developed by integrating the network data of both microbes and diseases to predict potential associations. The key innovations of this method include using the WKNKN algorithm to preprocess missing values in the association matrix and incorporating cross-domain information from microbes and diseases into the CMFHMDA model. To validate CMFHMDA, we employed three different cross-validation techniques to evaluate the model\u2019s accuracy. The results indicate that the CMFHMDA model achieved Area Under the Receiver Operating Characteristic Curve scores of 0.9172, 0.8551, and 0.9351$\\pm $0.0052 in global Leave-One-Out Cross-Validation (LOOCV), local LOOCV, and five-fold CV, respectively. Furthermore, many predicted associations have been confirmed by published experimental studies, establishing CMFHMDA as an effective tool for predicting potential disease-associated microbes.<\/jats:p>","DOI":"10.1093\/bib\/bbae481","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:27:26Z","timestamp":1727396846000},"source":"Crossref","is-referenced-by-count":11,"title":["CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization"],"prefix":"10.1093","volume":"25","author":[{"given":"Jing","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology , 215009 Suzhou ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology , 215009 Suzhou ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology , 215009 Suzhou ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Suzhou Research Center of Medical School, Suzhou Hospital , Affiliated Hospital of Medical School, , 215153 Suzhou ,","place":["China"]},{"name":"Nanjing University , Affiliated Hospital of Medical School, , 215153 Suzhou ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"2024092700272088400_ref1","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1038\/nature06244","article-title":"The Human Microbiome 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