{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:49Z","timestamp":1787027929067,"version":"build-2736575974"},"reference-count":46,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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 Na\u00efve 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\u2019s 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\u2019s test results found statistically significant differences between different Machine Learning approaches.<\/jats:p>","DOI":"10.3390\/diagnostics13172835","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T08:45:31Z","timestamp":1693557931000},"page":"2835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Crohn\u2019s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7545-2445","authenticated-orcid":false,"given":"Metehan","family":"Unal","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8547-7569","authenticated-orcid":false,"given":"Erkan","family":"Bostanci","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0921-5863","authenticated-orcid":false,"given":"Ceren","family":"Ozkul","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Microbiology, Faculty of Pharmacy, Hacettepe University, 06230 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3821-6419","authenticated-orcid":false,"given":"Koray","family":"Acici","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Engineering, Ankara University, 06830 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4153-0764","authenticated-orcid":false,"given":"Tunc","family":"Asuroglu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3408-0083","authenticated-orcid":false,"given":"Mehmet Serdar","family":"Guzel","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.chom.2014.02.005","article-title":"The treatment-naive microbiome in new-onset Crohn\u2019s disease","volume":"15","author":"Gevers","year":"2014","journal-title":"Cell Host Microbe"},{"key":"ref_2","unstructured":"National Library of Medicine (2023, July 05). 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