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Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition

  • 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.

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Metadaten
Author:Juliane PfeilORCiD, Julienne SiptrothORCiD, Heike Pospisil, Marcus FrohmeORCiDGND, Frank T. Hufert, Olga Moskalenko, Murad Yateem, Alina NechyporenkoORCiD
URN:urn:nbn:de:kobv:526-opus4-17184
DOI (Version of Record):https://doi.org/10.3390/bdcc7010051
ISSN:2504-2289
Parent Title (English):Big Data and Cognitive Computing
Publisher:MDPI
Document Type:Article
Language:English
Year of Publication:2023
Publishing Institution:Technische Hochschule Wildau
Release Date:2023/03/27
Tag:deep learning; human intestinal microbiome; image classification; next-generation sequencing; type 2 diabetes
Volume:7
Issue:1
Article Number:51
Source:Pfeil, J.; Siptroth, J.; Pospisil, H.; Frohme, M.; Hufert, F.T.; Moskalenko, O.; Yateem, M.; Nechyporenko, A. Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition. Big Data Cogn. Comput. 2023, 7, 51. https://doi.org/10.3390/bdcc7010051
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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