Version 1
: Received: 9 April 2023 / Approved: 11 April 2023 / Online: 11 April 2023 (08:14:43 CEST)
Version 2
: Received: 21 April 2023 / Approved: 23 April 2023 / Online: 23 April 2023 (02:59:58 CEST)
How to cite:
Zehrh, I.; Habiba, U.; Picco, M. R.; Basheer, S. H.; Rehman, U. A.; Haider, O.; Khoso, S. Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases. Preprints2023, 2023040209. https://doi.org/10.20944/preprints202304.0209.v1
Zehrh, I.; Habiba, U.; Picco, M. R.; Basheer, S. H.; Rehman, U. A.; Haider, O.; Khoso, S. Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases. Preprints 2023, 2023040209. https://doi.org/10.20944/preprints202304.0209.v1
Zehrh, I.; Habiba, U.; Picco, M. R.; Basheer, S. H.; Rehman, U. A.; Haider, O.; Khoso, S. Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases. Preprints2023, 2023040209. https://doi.org/10.20944/preprints202304.0209.v1
APA Style
Zehrh, I., Habiba, U., Picco, M. R., Basheer, S. H., Rehman, U. A., Haider, O., & Khoso, S. (2023). Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases. Preprints. https://doi.org/10.20944/preprints202304.0209.v1
Chicago/Turabian Style
Zehrh, I., Owaisa Haider and Shahzaib Khoso. 2023 "Metagenomics and Machine Learning-Based Precision Medicine Approaches for Autoimmune Diseases" Preprints. https://doi.org/10.20944/preprints202304.0209.v1
Abstract
The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.
Keywords
Gut microbiome; Machine learning; Deep learning; Metagenomics; Autoimmune diseases; Biomarkers discovery; Diagnostic models
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
