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Machine Learning Analysis of Inflammatory Bowel Disease-Associated Metagenomics Dataset

机译：炎症性肠病相关元基因组学数据集的机器学习分析

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There is an ongoing interplay between humans and our microbial communities. The microorganisms living in our gut produce energy from our food, strengthen our immune system, break down foreign products, and release metabolites and hormones, which are significant for regulating our physiology. The shifts away from this “healthy” gut microbiome is considered to be associated with many diseases. Inflammatory bowel diseases (IBD) including Crohn's disease and ulcerative colitis, are gut related disorders affecting the intestinal tract. Although some metagenomics studies are conducted on IBD recently, our current understanding of the precise relationships between the human gut microbiome and IBD remains limited. In this regard, the use of state-of-the art machine learning approaches became popular to address a variety of questions like early diagnosis of certain diseases using human microbiota. In this study, we investigate which subset of gut microbiota are mostly associated with IBD and if disease-associated biomarkers can be detected via applying state-of-the art machine learning algorithms and proper feature selection methods.
机译：人类与我们的微生物群落之间存在持续的相互作用。生活在肠道中的微生物从食物中产生能量，增强我们的免疫系统，分解异物，释放出代谢物和激素，这对于调节我们的生理至关重要。远离这种“健康”肠道微生物组的转变被认为与许多疾病有关。包括克罗恩氏病和溃疡性结肠炎在内的炎症性肠病（IBD）是影响肠道的肠道相关疾病。尽管最近在IBD上进行了一些宏基因组学研究，但我们目前对人肠道微生物组和IBD之间确切关系的理解仍然有限。在这方面，使用最新的机器学习方法来解决各种问题，例如使用人类微生物群对某些疾病的早期诊断。在这项研究中，我们调查了肠道菌群的哪些子集主要与IBD相关，以及是否可以通过应用最新的机器学习算法和适当的特征选择方法来检测与疾病相关的生物标记。

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