Machine Learning for the Analysis of Human Microbiome in Inflammatory Bowel Diseases: Literature Review
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Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47366-1_1
Reference21 articles.
1. Wright, E.K., Kamm, M.A., Teo, S.M., Inouye, M., Wagner, J., Kirkwood, C.D.: Recent advances in characterizing the gastrointestinal microbiome in Crohn’s disease: a systematic review. Inflamm. Bowel Dis. 21(6), 1219–1228 (2015). https://doi.org/10.1097/MIB.0000000000000382
2. Tefas, C., Ciobanu, L., Tantău, M., Moraru, C., Socaciu, C.: The potential of metabolic and lipid profiling in inflammatory bowel diseases: a pilot study. Bosnian J. Basic Med. Sci. 20(2), 262–270 (2020). https://doi.org/10.17305/bjbms.2019.423
3. Di Paola, M., et al.: Comparative immunophenotyping of Saccharomyces cerevisiae and Candida spp. strains from Crohn’s disease patients and their interactions with the gut microbiome. J. Trans. Autoimmunity 3, 100036 (2020). https://doi.org/10.1016/j.jtauto.2020.100036
4. Dong, L.N., Wang, M., Guo, J., Wang, J.P.: Role of intestinal microbiota and metabolites in inflammatory bowel disease. Chin. Med. J. 132(13), 1610–1614 (2019). https://doi.org/10.1097/CM9.0000000000000290
5. Rubbens, P., Props, R., Kerckhof, F.M., Boon, N., Waegeman, W.: Cytometric fingerprints of gut microbiota predict Crohn’s disease state. ISME J. 15(1), 354–358 (2021). https://doi.org/10.1038/s41396-020-00762-4
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