Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

中文翻译：

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来自组学数据的机器学习建模作为改进炎症性肠病诊断和临床分类的前瞻性工具

炎症性肠病 (IBD) 的研究已经确定了许多参与疾病发展的分子参与者。即便如此，对IBD的认识还不完整，而疾病治疗距离精准医疗还很遥远。IBD 中可靠的诊断和预后生物标志物是有限的，这可能会降低有效的治疗结果。高通量技术和人工智能成为寻找未揭示分子模式的强大工具，可以为 IBD 发病机制提供重要见解并帮助解决未满足的临床需求。机器学习是人工智能的一种子类型，它使用复杂的数学算法从现有数据中学习，以预测未来的结果。科学界越来越多地使用机器学习从全面的患者数据临床记录、基因组、转录组、蛋白质组、宏基因组和其他 IBD 相关组学数据中预测 IBD 结果。本综述旨在介绍机器学习建模背后的基本原理及其在 IBD 研究中的当前应用，重点是探索基因组和转录组数据的研究。我们描述了用于处理组学数据的不同策略，并概述了表现最佳的方法。在转化为临床环境之前，开发的机器学习模型应在独立的前瞻性研究和随机对照试验中进行测试。本综述旨在介绍机器学习建模背后的基本原理及其在 IBD 研究中的当前应用，重点是探索基因组和转录组数据的研究。我们描述了用于处理组学数据的不同策略，并概述了表现最佳的方法。在转化为临床环境之前，开发的机器学习模型应在独立的前瞻性研究和随机对照试验中进行测试。本综述旨在介绍机器学习建模背后的基本原理及其在 IBD 研究中的当前应用，重点是探索基因组和转录组数据的研究。我们描述了用于处理组学数据的不同策略，并概述了表现最佳的方法。在转化为临床环境之前，开发的机器学习模型应在独立的前瞻性研究和随机对照试验中进行测试。