组学大数据和医学人工智能

doi:10.16288/j.yczz.21-215

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

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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)

Abstract

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

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

Figures/Tables 2

Fig. 1

Table 1

References 30

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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]

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