利用人工智能进行基因组变异预测:进展,挑战和未来方向
Indah Pakpahan1,2,3, Mentari Sihombing1,2,4, Haohan Liu2
1Dalian University of Technology, Dalian 116000, China.
GigaScience
|January 10, 2026
概括
人工智能 (AI) 推进了疾病研究的遗传变异解释. 这篇评论强调了人工智能.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 精确的基因变异解释对于推进疾病研究和开发向疗法至关重要.
- 传统的变异解释方法面临复杂的基因组数据和需要个性化医疗方法的挑战.
研究的目的:
- 审查人工智能 (AI) 方法论在遗传变异解释中的演变.
- 确定关键挑战,并提出改善变异分类准确性和临床实用性的策略.
主要方法:
- 审查传统的基于规则的系统,统计模型和当代人工智能方法,包括机器学习,深度学习和蛋白质语言模型.
- 对当前变异影响预测器 (VIPs) 和它们的局限性进行分析.
- 评估诸如数据异质性,可解释性和不确定的意义变体 (VUS) 等挑战.
主要成果:
- 人工智能,包括深度学习和蛋白质语言模型,越来越多地被整合到变体解释中.
- 仍然存在重大挑战,包括数据异质性,模型可解释性和VUS的流行率.
- 需要可解释的人工智能和更具包容性的基因组数据库,以提高不同人群的预测准确性.
结论:
- 人工智能为提高遗传变异解释准确性提供了强大的工具.
- 提出了改进预测器选择,多omics数据集成和优化工作流程的策略.
- 增强的变体解释将加速个性化医学在研究和临床实践中的进步.
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