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Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome
Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486516/ https://www.ncbi.nlm.nih.gov/pubmed/37685376 http://dx.doi.org/10.3390/diagnostics13172835 |
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author | Unal, Metehan Bostanci, Erkan Ozkul, Ceren Acici, Koray Asuroglu, Tunc Guzel, Mehmet Serdar |
author_facet | Unal, Metehan Bostanci, Erkan Ozkul, Ceren Acici, Koray Asuroglu, Tunc Guzel, Mehmet Serdar |
author_sort | Unal, Metehan |
collection | PubMed |
description | Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar’s test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar’s test results found statistically significant differences between different Machine Learning approaches. |
format | Online Article Text |
id | pubmed-10486516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104865162023-09-09 Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome Unal, Metehan Bostanci, Erkan Ozkul, Ceren Acici, Koray Asuroglu, Tunc Guzel, Mehmet Serdar Diagnostics (Basel) Article Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data. The dataset was obtained from NCBI preprocessed graph representations and converted into a structured form. Seven well-known Machine Learning frameworks, including Random Forest, Support Vector Machines, Extreme Gradient Boosting, Light Gradient Boosting Machine, Gaussian Naïve Bayes, Logistic Regression, and k-Nearest Neighbor, were used. Grid Search was employed for hyperparameter optimization. The performance of the Machine Learning models was evaluated using various metrics such as accuracy, precision, fscore, kappa, and area under the receiver operating characteristic curve. Additionally, Mc Nemar’s test was conducted to assess the statistical significance of the experiment. The data was constructed using k-mer lengths of 3, 4 and 5. The Light Gradient Boosting Machine model overperformed over other models with 67.24%, 74.63% and 76.47% accuracy for k-mer lengths of 3, 4 and 5, respectively. The LightGBM model also demonstrated the best performance in each metric. The study showed promising results predicting disease from raw sequence data. Finally, Mc Nemar’s test results found statistically significant differences between different Machine Learning approaches. MDPI 2023-09-01 /pmc/articles/PMC10486516/ /pubmed/37685376 http://dx.doi.org/10.3390/diagnostics13172835 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Unal, Metehan Bostanci, Erkan Ozkul, Ceren Acici, Koray Asuroglu, Tunc Guzel, Mehmet Serdar Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome |
title | Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome |
title_full | Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome |
title_fullStr | Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome |
title_full_unstemmed | Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome |
title_short | Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome |
title_sort | crohn’s disease prediction using sequence based machine learning analysis of human microbiome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486516/ https://www.ncbi.nlm.nih.gov/pubmed/37685376 http://dx.doi.org/10.3390/diagnostics13172835 |
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