从功能到翻译:通过人工智能解码人类疾病的遗传易感性
Erping Long1,2, Peixing Wan3, Qingyu Chen4
1Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cell genomics
|June 30, 2023
概括
人工智能 (AI) 正在通过解码复杂的功能基因组学数据来推进全基因组关联研究 (GWAS),以揭示疾病机制并将发现转化为临床应用. 这种方法解决了GWAS后解释数据异质性和维度方面的挑战.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 全基因组关联研究 (GWAS) 确定了许多与疾病相关的遗传位置,但它们的分子机制往往仍然不清楚.
- 解释这些遗传关联 (功能研究) 并将其转化为临床益处 (转化研究) 是GWAS后的关键步骤.
- 现有的功能基因组学方法由于数据异质性,多重性和高维性而面临挑战.
研究的目的:
- 审查人工智能 (AI) 在解释和翻译GWAS发现方面的进展.
- 突出挑战,并为人工智能驱动的GWAS后研究提供建议.
- 讨论将人工智能应用于GWAS数据的伦理考虑.
主要方法:
- 在功能性基因组学中对人工智能应用进行审查,以解释GWAS.
- 分析数据处理和模型优化对GWAS中的AI的挑战.
- 讨论道德含义和未来的方向.
主要成果:
- 人工智能在为GWAS解码复杂的功能基因组学数据集方面显示出显著的希望.
- 人工智能有助于理解疾病相关位置的基础分子机制.
- 人工智能有助于将GWAS发现转化为潜在的临床益处.
结论:
- 人工智能是克服GWAS后口译和翻译方面的挑战的强大工具.
- 需要进一步发展数据可用性,模型优化和解释.
- 在GWAS研究中负责任地实施人工智能时,道德考虑至关重要.
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