{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:33:45Z","timestamp":1783488825337,"version":"3.55.0"},"reference-count":54,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Federal Ministry of Education and Research","doi-asserted-by":"publisher","award":["13FH209PX8"],"award-info":[{"award-number":["13FH209PX8"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.<\/jats:p>","DOI":"10.3390\/bdcc7010051","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T02:59:26Z","timestamp":1679021966000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4846-1495","authenticated-orcid":false,"given":"Juliane","family":"Pfeil","sequence":"first","affiliation":[{"name":"Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4888-5234","authenticated-orcid":false,"given":"Julienne","family":"Siptroth","sequence":"additional","affiliation":[{"name":"Division High Performance Computing in Life Sciences, Technical University of Applied Sciences, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heike","family":"Pospisil","sequence":"additional","affiliation":[{"name":"Division High Performance Computing in Life Sciences, Technical University of Applied Sciences, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9063-2682","authenticated-orcid":false,"given":"Marcus","family":"Frohme","sequence":"additional","affiliation":[{"name":"Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank T.","family":"Hufert","sequence":"additional","affiliation":[{"name":"Institute for Microbiology and Virology, Brandenburg Medical School Theodor Fontane, 16816 Neuruppin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Moskalenko","sequence":"additional","affiliation":[{"name":"BIOMES NGS GmbH, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murad","family":"Yateem","sequence":"additional","affiliation":[{"name":"BIOMES NGS GmbH, 15745 Wildau, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4501-7426","authenticated-orcid":false,"given":"Alina","family":"Nechyporenko","sequence":"additional","affiliation":[{"name":"Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences, 15745 Wildau, Germany"},{"name":"Department of Systems Engineering, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s41579-020-0433-9","article-title":"Gut Microbiota in Human Metabolic Health and Disease","volume":"19","author":"Fan","year":"2021","journal-title":"Nat. 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