对于卵巢癌中单个omics的人工智能:一个方法审查
Pilar Ordás1, Jose Crossa2, Luis Chiva1
1Clínica Universidad de Navarra, Department of Gynecology and Obstetrics, Madrid, Spain.
概括
人工智能 (AI) 与omics数据相结合,有望改善卵巢癌的诊断和治疗. 然而,目前的AI模型需要更大的研究和更好的临床验证.
科学领域:
- 在瘤学瘤学.
- 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:
- 卵巢癌带来了严重的死亡率挑战,特别是在晚期.
- 人工智能 (AI) 与omics数据的整合为改善卵巢癌诊断,预后和治疗提供了新的途径.
- 卵巢上皮癌 (EOC) 仍然是研究的关键领域,因为它的结果很差.
研究的目的:
- 审查最近 (2021-2024) 应用人工智能对EOC的各种omics数据的进展.
- 评估用于EOC检测,化疗反应预测和遗传风险分层的AI模型.
- 确定人工智能在个性化卵巢癌医学中的方法挑战和未来方向.
主要方法:
- 在2021年至2024年间发表的14项研究的叙述性综述.
- 在EOC患者的基因组,转录组,代谢组,微生物组和表观基因组数据中分析AI应用.
- 专注于用于分类和预测任务的AI模型.
主要成果:
- 人工智能模型在几个研究中显示出高分类准确度和曲线下面积值.
- 通常使用的AI模型包括深度学习,随机森林和支持矢量机器.
- 发现了重要的局限性:小样本大小,回顾性设计和不一致的验证.
结论:
- 基于人工智能的方法具有很大的潜力,可以在卵巢癌中推进个性化医学.
- 数据预处理和特征选择等方法学考虑对于AI模型性能至关重要.
- 未来的研究需要更大规模的,多中心的,前性研究,具有强大的验证和改善临床翻译的可解释性.
关键词:
人工智能的人工智能生物标志物 生物标志物化疗反应对化疗的反应深度学习 (Deep Learning) 是一种深度学习.机器学习 机器学习俄米克斯 (Omics) 是一个电子游戏.卵巢癌 卵巢癌 卵巢癌更多相关视频
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