Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks
- Background Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients. Methods Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways. Results It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674). Conclusion With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis.
Author: | Julienne SiptrothORCiD, Olga Moskalenko, Carsten Krumbiegel, Jörg Ackermann, Ina Koch, Heike Pospisil |
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URN: | urn:nbn:de:kobv:526-opus4-17497 |
DOI (Version of Record): | https://doi.org/10.1007/s44163-023-00064-6 |
ISSN: | 2731-0809 |
Parent Title (English): | Discover Artificial Intelligence |
Publisher: | Springer Nature |
Document Type: | Article |
Language: | English |
Year of Publication: | 2023 |
Publishing Institution: | Technische Hochschule Wildau |
Release Date: | 2023/05/15 |
Tag: | NGS; artificial neural network; explainable artificial intelligence (XAI); gut; machine learning; type 2 diabetes mellitus |
Volume: | 3 |
Issue: | 1 |
Article Number: | 19 |
Source: | Siptroth, J., Moskalenko, O., Krumbiegel, C. et al. Investigation of metabolic pathways from gut microbiome analyses regarding type 2 diabetes mellitus using artificial neural networks. Discov Artif Intell 3, 19 (2023). https://doi.org/10.1007/s44163-023-00064-6 |
Faculties an central facilities: | Fachbereich Ingenieur- und Naturwissenschaften |
Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Funding: | Projekt DEAL |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |