The Development and Prospect of Biomedical Informatics Driven by Data and Technology

doi:10.11871/jfdc.issn.2096-742X.2023.01.004

Abstract

Abstract:

[Objective] Biomedical informatics is one of the fastest specialized developing disciplines of information sciences. Our review summarizes the latest progress and future development of theoretical systems, platforms, and tools of biomedical informatics. [Methods] This review teases out the development of data resource construction, data analysis algorithms and models, tools, and software in biomedical informatics through bibliometrics and literature research. [Results] It will effectively support the development of biomedical informatics by forming a discipline development path, which is resource-based, technology-supported, talent-centered, and application-oriented. [Limitations] Limited by word count, some biomedical data resources and methods are not thoroughly involved. [Conclusions] Data and technology are key elements in the development of biomedical informatics. We hope to provide a literature review and references for the improvement of biomedical informatics in China.

Key words: Biomedical Informatics, knowledge discovery, data resource, computing models, algorithms and tools

FAN Shaoping,ZHANG Zhiqiang. The Development and Prospect of Biomedical Informatics Driven by Data and Technology[J]. Frontiers of Data and Computing, 2023, 5(1): 41-54, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.01.004.

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界定	内容
学科范围	研究并支持从分子到个体乃至人群，从生物到社会系统的推理、建模、模拟、实验和翻译，将基础和临床研究与实践同医疗卫生企业联系起来。
理论方法	开发、研究和应用理论、方法和过程以生成、存储、检索、使用、管理和共享生物医学数据、信息和知识。
技术方法	建立在计算机、通信和信息科学与技术基础上并对其做出贡献，强调它们在生物医学中的应用。
社会背景	认识到人类是生物医学信息学的最终用户，利用社会和行为科学，为技术解决方案、政策设计和评估以及经济、伦理、社会、教育和组织系统的演变提供信息。

序号	类团名称	主要关键词
1	癌症研究的生物医学信息学	表达、肿瘤、小RNA、乳腺癌、长链非编码RNA、转移、突变、细胞、生物标志物、激活、肝细胞癌、信使RNA、RNA、特异性、甲基化等
2	组学数据库等资源研究	数据库、识别、发现、网络、资源、基因组学、全基因组关联、蛋白质组学、代谢组学、转录组学等
3	算法与模型研究	蛋白质、预测、机器学习、算法、分类、模型、2019冠状病毒疾病、多序列比对、SARS冠状病毒2型、大数据、神经网络等
4	分析工具/软件研究	序列、工具、基因组、对齐、宏基因组学、软件、网络服务器、遗传学、搜索等

序号	数据库名称	URL
1	COVID19db	http://www.biomedical-web.com/cov-id19db or http://hpcc.siat.ac.cn/cov-id19db
2	Ensembl COVID-19 resource	https://covid-19.ensembl.org
3	ESC	http://clingen.igib.res.in/esc
4	SCoV2-MD	http://www.scov2-md.org
5	SCovid	http://bio-annotation.cn/scovid
6	T-cell COVID-19 Atlas	https://t-cov.hse.ru
7	VarEPS	https://nmdc.cn/ncovn

任务	模型/方法	语料库	最佳F1值
命名实体识别	BioBERT^[24]	NCBI Disease	89.71%
		2010 i2b2/VA	86.73%
		BC5CDR（Disease）	87.15%
		BC5CDR（Drug/Chem）	93.47%
		BC4CHEMD	92.36%
		BC2GM	84.72%
		JNLPBA	77.49%
		LINNAEUS	88.24%
		Species-800	74.06%
	BioBERT+Attention-based BiLSTM-CRF^[25]	BC5CDR（Disease）	88.34%
		NCBI Disease	91.23%
		BC5CDR（Drug/Chem）	94.23%
		BC4CHEMD	92.28%
		JNLPBA	79.97%
		BC2GM	86.05%
	BIOKMNER^[26]	BC2GM	85.29%
		JNLPBA	77.83%
		BC5CDR（Drug/Chem）	94.22%
		NCBI Disease	89.63%
		LINNAEUS	89.24%
		Species-800	76.33%
文本分类	MeSHProbeNet^[27]	2018 BioASQ	68.80%
	FullMeSH^[28]	PMC	66.76%
	BERTMeSH^[29]	PMC	69.20%
关系抽取	KCN^[30]	BioCreative V CDR	71.28%
	KGAGN^[31]	BioCreative V CDR	73.30%
	PACNN+RL^[32]	Prevent	55.92%
		Treat	66.66%
		DDI corpus	38.38%
		AIMed	44.72%
		BioInfer	52.09%
		HPRD50	65.40%
		IEPA	65.54%
		LLL	67.09%
通路提取	远程监督方法^[33]	PubMed	精度为25%
预测	DPDDI^[34]	DB2	84.00%
	NNPS^[35]	TWOSIDES	93.60%
	Node similarity-based-NN^[36]	Drug Bank DDI	AUC为0.933±0.003
		CTD DDA	AUC为0.950±0.004
		NDFRT DDA	AUC为0.943±0.004
		STRING PPI	AUC为0.952±0.004

任务	工具	URL
蛋白质结构/功能预测工具	GalaxyHeteromer	http://galaxy.seoklab.org/heteromer
蛋白质结构/功能预测工具	DeepGOWeb	https://deepgo.cbrc.kaust.edu.sa/deepgo/
多组学分析工具	Mergeomics	http://mergeomics.research.idre.ucla.edu/
多组学分析工具	iNetModels	https://inetmodels.com
药物分析工具	DrugComb	https://drugcomb.org/
药物分析工具	LigAdvisor	https://ligadvisor.unimore.it/