Artificial Intelligence-Biology: An Important Strategic Resource and Competitive Hot Spot in the Future Research of TCM Modernization
-
摘要: 中医药现代化在二十年的发展过程中取得了一些成果，但仍存在诸多障碍。随着人工智能技术在生物科学领域研究中的应用(包括蛋白结构解析、新药研发等)，或许可以为突破制约中医药现代化的障碍带来新的研究思路和策略。中医药临床数据及研究数据的不断积累，也为开展人工智能与中医药的融合研究提供了基础。该文分析了人工智能助力中医药研究的热点与趋势，并进一步探讨了人工智能技术在中医大家学术思想传承与创新、中医证候治则治法的现代生物学基础、中药物质基础及起效机制等中医药关键科学问题研究中的推动作用，这可能是未来中医药现代化的战略资源和竞争赛点。Abstract: The modernization of traditional Chinese medicine (TCM) has made some achievements in the past two decades, but there are still many obstacles. With the application of artificial intelligence(AI)technology in the field of Biological Science (including protein structure analysis, new drug development, etc.), it may bring new research ideas and strategies for the breakthrough of obstacles restricting the modernization of TCM. The continuous accumulation of clinical and research data of TCM also provides the basis for the integration of AI and TCM. This article analyzes the hotspot and forward of AI in the field of TCM, then further investigates the enormous impetus of AI technology in the research of key scientific issues of TCM, such as the inheritance and innovation of academic ideas of TCM masters, the modern biological basis of TCM therapeutic principle, the material basis and mechanism of action of TCM. Those may be the strategic resource and competition point for the modernization of TCM in the future.
-
[1] 胡晓翔. 整体观下之辨证论治: 《中华人民共和国中医药法》评析[J]. 南京中医药大学学报(社会科学版), 2017, 18(1): 14-19. https://www.cnki.com.cn/Article/CJFDTOTAL-NJZD201701003.htm [2] 陆茵, 王爱云, 韦忠红, 等. 基于"整体观"探讨中药的研究思维和方法[J]. 世界科学技术-中医药现代化, 2019, 21(1): 1-7. https://www.cnki.com.cn/Article/CJFDTOTAL-SJKX201901003.htm [3] 林树元, 瞿溢谦, 刘畅, 等. 中医药人工智能发展述评及技术融合趋势探讨[J]. 中华中医药杂志, 2020, 35(11): 5384-5389. https://www.cnki.com.cn/Article/CJFDTOTAL-BXYY202011012.htm [4] 于观贞, 田建辉, 钱建新, 等. 人工智能促进传统中医药向现代中医药转变[C]//第十七届全国中西医结合肿瘤学术大会. 中国中西医结合学会会议论文集, 2019: 72. [5] 刘辰. 国务院印发《新一代人工智能发展规划》: 构筑我国人工智能发展先发优势[J]. 中国科技产业, 2017(8): 78-79. doi: 10.3969/j.issn.1671-2064.2017.08.060 [6] 黄蓓. 《中共中央国务院关于促进中医药传承创新发展的意见》重点任务分工方案印发[J]. 中医药管理杂志, 2019, 27(24): 2. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYYG201924001.htm [7] 陈瑶, 曾雪扬, 杨丰文, 等. 基于CiteSpace的中医药领域网状Meta分析的研究热点可视化分析[J]. 中国中药杂志, 2020, 45(18): 4500-4509. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGZY202018028.htm [8] 张林力, 张润顺, 姚乃礼, 等. 名老中医医案研究现状、质量问题及应对策略[J]. 世界科学技术-中医药现代化, 2020, 22(5): 1608-1613. https://www.cnki.com.cn/Article/CJFDTOTAL-SJKX202005036.htm [9] 沈春锋, 王彩华, 陆炜青, 等. 名老中医传承中的隐性知识挖掘[J]. 中医杂志, 2016, 57(11): 930-932. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZYZ201611009.htm [10] 邱德胜, 罗译泓. 基于人工智能的中医有效性探究[J]. 自然辩证法研究, 2020, 36(9): 64-69. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRBZ202009012.htm [11] 廖娟, 王建超, 玉叶, 等. 人工智能在名老中医传承中的应用[J]. 中华中医药杂志, 2020, 35(4): 1671-1674. https://www.cnki.com.cn/Article/CJFDTOTAL-BXYY202004012.htm [12] 张柯欣, 孙艳秋. 中医人工智能模型的设计要素研究[J]. 中华中医药学刊, 2020, 38(12): 116-118. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYHS202012031.htm [13] LIU C, SUN J. AI in Measurement Science[J/OL]. Annu Rev Anal Chem, 2021. https://pubmed.ncbi.nlm.nih.gov/33900824. [14] KEENAN EK, ZACHMAN DK, HIRSCHEY MD. Discovering the landscape of protein modifications[J/OL]. Mol Cell, 2021. https://www.sciencedirect.com/science/article/pii/S1097276521002100. [15] TATARU OS, VARTOLOMEI MD, RASSWEILER JJ, et al. Artificial intelligence and machine learning in prostate cancer patient management-current trends and future perspectives[J]. Diagnostics, 2021, 11(2): 1-20. http://www.researchgate.net/publication/349457332_Artificial_Intelligence_and_Machine_Learning_in_Prostate_Cancer_Patient_Management-Current_Trends_and_Future_Perspectives [16] LAU HJ, LIM CH, FOO SC, et al. The role of artificial intelligence in the battle against antimicrobial-resistant bacteria[J/OL]. Curr Genet, 2021. DOI: 10.1007/s00294-021-01156-5. [17] SENIOR AW, EVANS R, JUMPER J, et al. Improved protein structure prediction using potentials from deep learning. Nature, 2020, 577(7792): 706-710. doi: 10.1038/s41586-019-1923-7 [18] HEY T, BUTLER K, JACKSON S, et al. Machine learning and big scientific data[J]. Philos Trans A Math Phys Eng Sci, 2020, 378(2166): 20190054. http://arxiv.org/abs/1910.07631 [19] JIMÉNEZ-LUNA J, GRISONI F, WESKAMP N, et al. Artificial intelligence in drug discovery: Recent advances and future perspectives[J]. Expert Opin Drug Discov, 2021: 1-11. doi: 10.1080/17460441.2021.1909567 [20] 韦忠红, 余苏云, 陈文星, 等. 微量中药成分的起效机制研究不容忽视: "遗传协同致死"模式有助于发现并阐释中药显效物质基础及起效机制[J]. 世界科学技术-中医药现代化, 2017, 19(9): 1424-1429. https://www.cnki.com.cn/Article/CJFDTOTAL-SJKX201709004.htm [21] ANDRONIS C, SHARMA A, VIRVILIS V, et al. Literature mining, ontologies and information visualization for drug repurposing[J]. Brief Bioinform, 2011, 12(4): 357-368. doi: 10.1093/bib/bbr005 [22] WU J, ZHANG T, YU L, et al. Zhile capsule exerts antidepressant-like effects through upregulation of the BDNF signaling pathway and neuroprotection[J]. Int J Mol Sci, 2019, 20(1): 1-10. http://www.mdpi.com/1422-0067/20/1/195/pdf [23] 陆茵, 岳志强, 刘玉萍, 等. 基于生物网络调控下的中药复方多靶整合作用的研究思路与方法[J]. 中药药理与临床, 2012, 28(5): 240-241. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYYL201205090.htm [24] 陶丽, 范方田, 刘玉萍, 等. 中药及其组分配伍的整合作用研究实践与进展[J]. 中国药理学通报, 2013, 29(2): 153-156. doi: 10.3969/j.issn.1001-1978.2013.02.02 [25] 陈文星, 王旭, 王爱云, 等. 基于蛋白信号转导网络的组分中药研究方法学[J]. 世界科学技术-中医药现代化, 2017, 19(5): 725-729. doi: 10.11842/wst.2017.05.001 [26] DUAN D, DOAK AK, NEDYALKOVA L, et al. Colloidal aggregation and the in vitro activity of traditional Chinese medicines[J]. ACS Chem Biol, 2015, 10(4): 978-988. doi: 10.1021/cb5009487 [27] 刘钰涵, 胡芳. 人工智能时代创新型中医药数据分析人才的培养模式研究[J]. 学校党建与思想教育, 2020(16): 71-73. https://www.cnki.com.cn/Article/CJFDTOTAL-XXDJ202016026.htm -
下载:
点击查看大图
图(3)
计量
- 文章访问数: 684
- HTML全文浏览量: 69
- PDF下载量: 210
- 被引次数: 0
