组学大数据和医学人工智能

doi:10.16288/j.yczz.21-215

遗传 ›› 2021, Vol. 43 ›› Issue (10): 930-937.doi: 10.16288/j.yczz.21-215

组学大数据和医学人工智能

王昕玥^1,^2,³(), 渠鸿竹^1,^2,³(), 方向东^1,^2,³()

1. 中国科学院北京基因组研究所(国家生物信息中心),中国科学院基因组科学与信息重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 中国科学院北京基因组研究所(国家生物信息中心),基因组与精准医学检测技术北京市重点实验室,北京 100101;

收稿日期:2021-06-18 修回日期:2021-08-31 出版日期:2021-10-20 发布日期:2021-10-08
通讯作者: 渠鸿竹,方向东 E-mail:wangxinyue2019d@big.ac.cn;quhongzhu@big.ac.cn;fangxd@big.ac.cn
作者简介:王昕玥,在读博士研究生,研究方向：基因组学。E-mail: wangxinyue2019d@big.ac.cn
基金资助:
科技部重点研发计划资助编号(2020YFC2003405);科技部重点研发计划资助编号(2016YFC0901700);科技部重点研发计划资助编号(2018YFC0910700)

Omics big data and medical artificial intelligence

Xinyue Wang^1,^2,³(), Hongzhu Qu^1,^2,³(), Xiangdong Fang^1,^2,³()

1. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Beijing Key Laboratory of Genome and Precision Medicine Technologies, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing 100101, China

Received:2021-06-18 Revised:2021-08-31 Online:2021-10-20 Published:2021-10-08
Contact: Qu Hongzhu,Fang Xiangdong E-mail:wangxinyue2019d@big.ac.cn;quhongzhu@big.ac.cn;fangxd@big.ac.cn
Supported by:
Supported by the National Key Research and Development Project of the Ministry of Science and Technology of the People’s Republic of China Nos(2020YFC2003405);Supported by the National Key Research and Development Project of the Ministry of Science and Technology of the People’s Republic of China Nos(2016YFC0901700);Supported by the National Key Research and Development Project of the Ministry of Science and Technology of the People’s Republic of China Nos(2018YFC0910700)

摘要/Abstract

摘要：

随着高通量测序技术和计算机科学的飞速发展,组学数据量指数倍增长,多组学分析优势逐渐显现,人工智能应用也愈加广泛。本文介绍了近年来多组学数据分析和人工智能各自在医学领域的应用进展,同时也介绍了两者相结合应用的案例以及优势,最后简单阐述多组学分析和人工智能在现阶段面临的挑战,旨在为医学行业提供新的研究思路,助推精准医学发展应用。

关键词: 多组学, 人工智能, 医学, 精准医学

Abstract:

With the rapid development of high-throughput sequencing technology and computer science, the amount of large omics data has increased exponentially, the advantages of multi-omics analysis have gradually emerged, and the application of artificial intelligence has become more and more extensive. In this review, we introduce the application progress of multi-omics data analysis and artificial intelligence in the medical field in recent years, and also show the cases and advantages of their combined application. Finally, we briefly explain the current challenges of multi-omics analysis and artificial intelligence in order to provide new research ideas for the medical industry and to promote the development and application of precision medicine.

Key words: multi-omics, artificial intelligence, medicine, precision medicine

王昕玥, 渠鸿竹, 方向东. 组学大数据和医学人工智能[J]. 遗传, 2021, 43(10): 930-937.

Xinyue Wang, Hongzhu Qu, Xiangdong Fang. Omics big data and medical artificial intelligence[J]. Hereditas(Beijing), 2021, 43(10): 930-937.

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应用	研究内容	方法	结果	啊啊啊啊
影像	黄斑变性	DL_OVLP	单模态纵向配准实验平均误差为54~59 µm,多模态横断面配准实验平均误差为66~69 µm	[12]
	乳腺DCE MRI	AI系统	AI分析平均AUC从0.71提高到0.76	[13]
	分割脑部MRI图像	大容量深度卷积神经网络	完整区域,核心区域和增强区域的准确性分别为0.90、0.85和0.84	[14]
癌症研究	口腔癌诊断	分层深度卷积神经网络	准确度为94.5%	[15]
	上消化道癌诊断	GRAIDS	内部验证集准确性为0.955,外部验证集准确性结果为0.915~0.977,且灵敏度堪比专业内镜医师	[16]
	胃粘膜病变	卷积神经网络	敏感性显著高于专家组结果,准确率和特异性与专家组未有差异	[17]
	黑色素瘤图像	卷积神经网络	CNN的灵敏度和特异性均高于专家	[18]
	结直肠癌转移至淋巴结风险	人工神经网络	AUC=0.84	[19]
	前列腺活检评分	深度学习	良恶性AUC=0.990 (0.982~0.996);观察者数据集中,kappa比专家组高	[20]
	乳腺癌预后预测	基于堆叠集成的卷积神经网络	AUC=0.93,准确度为90.2%	[21]
辅助医学	预测术后30天死亡率	多路径卷积神经网络	AUC=0.867 (0.835~0.899)	[22]
辅助医学	测量视网膜血管口径	SIVA-DLS	与人工测量结果相关系数在0.82~0.95之间	[23]

组学大数据和医学人工智能

Omics big data and medical artificial intelligence

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