炎症性肠病的基因组学,转录基因组学,蛋白质组学和转基因组学与人工智能相遇
Anna Lucia Cannarozzi1, Anna Latiano1, Luca Massimino2
1Division of Gastroenterology and Endoscopy, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
United European gastroenterology journal
|August 31, 2024
概括
炎症性肠病 (IBD) 涉及复杂的遗传和环境因素. 多态和人工智能 (AI) 提供了了解IBD的新方法,为精准医学铺平了道路.
科学领域:
- 胃肠道学和免疫学
- 计算生物学和生物信息学
背景情况:
- 炎症性肠病 (IBD),包括性结肠炎和克罗恩病,是由遗传,环境和生活方式因素的复杂相互作用引起的.
- IBD的异质性在改善疾病管理,治疗策略以及了解遗传和暴露体影响方面提出了重大挑战.
研究的目的:
- 审查IBD研究中的当前进展,利用omics技术 (基因组学,转录组学,蛋白质组学,元基因组学) 和多omics方法.
- 探索人工智能 (AI) 在整合复杂,异质数据集中的作用,以更深入地了解IBD病原和进展.
- 识别知识差距,并为IBD的未来研究方向提供信息,旨在实现精准医学.
主要方法:
- 对IBD中的单个omics (基因组学,转录组学,蛋白质组学,元基因组学) 和多omics研究的综合文献综述.
- 分析系统生物学工具和人工智能 (AI) 在处理大规模生物和临床数据集中的应用.
- 综合当前的理解和识别的研究差距,基于文献的综述.
主要成果:
- 多学科研究和人工智能方法对于破译影响IBD的复杂相互作用至关重要.
- 这些由大数据驱动的创新方法提高了对疾病自然史,预测因素,严重程度,并发症和治疗结果的理解.
- 人工智能有助于整合各种数据类型,为临床医生提供决策支持,并在IBD中推进精准医学.
结论:
- 将omics数据与人工智能的整合为揭示IBD复杂性提供了重大前景.
- 这种协同方法对于开发更有效的诊断和治疗策略至关重要,最终可以改善IBD的患者护理和结果.
- 需要进一步的研究,以充分利用这些技术在IBD个性化治疗方法.
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