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However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publicly available datasets of six different diseases. In particular, it performs best with the area under the receiver operating characteristic curve of 0.9182\u2009\u00b1\u20090.0098 and 0.9469\u2009\u00b1\u20090.0076 for inflammatory bowel disease (IBD) and liver cirrhosis, respectively. Our MicroHDF also shows better performance and robustness in cross-study validation. Furthermore, MicroHDF is applied to two high-risk diseases, IBD and autism spectrum disorder, as case studies to identify potential biomarkers. In conclusion, our method provides an effective and reliable prediction of the host phenotype and discovers informative features with biological insights.<\/jats:p>","DOI":"10.1093\/bib\/bbae530","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T13:14:34Z","timestamp":1729775674000},"source":"Crossref","is-referenced-by-count":7,"title":["MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0038-625X","authenticated-orcid":false,"given":"Kai","family":"Shi","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology , Guilin, Gaungxi 541004 ,","place":["China"]},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology , Guilin, Gaungxi 541004 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology , Guilin, Gaungxi 541004 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingrong","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology , Guilin, Gaungxi 541004 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qisheng","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology , Guilin, Gaungxi 541004 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Huzhou Central Hospital, Affiliated Central Hospital Huzhou University , Huzhou, Zhejiang 313000 ,","place":["China"]},{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University , Shanghai 200433 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"2024102415561856400_ref1","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1146\/annurev-physiol-031522-092054","article-title":"The role of the gut microbiota in the relationship between diet and human health","volume":"85","author":"Perler","year":"2023","journal-title":"Annu Rev Physiol"},{"issue":"3","key":"2024102415561856400_ref2","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/s41581-022-00654-0","article-title":"The gut microbiome and hypertension","volume":"19","author":"O'Donnell","year":"2023","journal-title":"Nat Rev 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