• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

Grand Challenges for Artificial Intelligence in Molecular Medicine.

作者信息

Emmert-Streib Frank

机构信息

Predictive Society and Data Analytics Lab, Faculty of Information Technolgy and Communication Sciences, Tampere University, Tampere, Finland.

Institute of Biosciences and Medical Technology, Tampere, Finland.

出版信息

Front Mol Med. 2021 Jul 22;1:734659. doi: 10.3389/fmmed.2021.734659. eCollection 2021.

DOI:10.3389/fmmed.2021.734659
PMID:39087080
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11285658/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a58/11285658/f426cf8b207f/fmmed-01-734659-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a58/11285658/f426cf8b207f/fmmed-01-734659-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a58/11285658/f426cf8b207f/fmmed-01-734659-g001.jpg

相似文献

1
Grand Challenges for Artificial Intelligence in Molecular Medicine.分子医学中人工智能面临的重大挑战。
Front Mol Med. 2021 Jul 22;1:734659. doi: 10.3389/fmmed.2021.734659. eCollection 2021.
2
Machine learning meets omics: applications and perspectives.机器学习与组学的融合:应用与展望。
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab460.
3
Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.利用人工智能、机器学习和组学重新思考药物重定位和开发。
OMICS. 2019 Nov;23(11):539-548. doi: 10.1089/omi.2019.0151. Epub 2019 Oct 25.
4
Perspectives on development of biomedical polymer materials in artificial intelligence age.人工智能时代生物医用高分子材料的发展展望。
J Biomater Appl. 2023 Mar;37(8):1355-1375. doi: 10.1177/08853282231151822. Epub 2023 Jan 11.
5
Common Pitfalls and Recommendations for Grand Challenges in Medical Artificial Intelligence.医学人工智能大挑战中的常见陷阱和建议。
Eur Urol Focus. 2021 Jul;7(4):710-712. doi: 10.1016/j.euf.2021.05.008. Epub 2021 Jun 11.
6
Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation.数据科学、人工智能与机器学习:检验医学的机遇及积极监管的价值
Clin Biochem. 2019 Jul;69:1-7. doi: 10.1016/j.clinbiochem.2019.04.013. Epub 2019 Apr 22.
7
Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art - with Reflections on Present AIM Challenges.医学人工智能的起源(AIM):辅助科学探究与临床实践的计算手段——兼论当前AIM面临的挑战
Yearb Med Inform. 2019 Aug;28(1):249-256. doi: 10.1055/s-0039-1677895. Epub 2019 Apr 25.
8
Omics big data and medical artificial intelligence.组学大数据与医疗人工智能。
Yi Chuan. 2021 Oct 20;43(10):930-937. doi: 10.16288/j.yczz.21-215.
9
Opportunities for Artificial Intelligence in Advancing Precision Medicine.人工智能在推进精准医学方面的机遇。
Curr Genet Med Rep. 2019 Dec;7(4):208-213. doi: 10.1007/s40142-019-00177-4. Epub 2019 Dec 1.
10
Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine.人工智能在医疗保健数据管理中的展望:迈向精准医学的旅程。
Comput Biol Med. 2023 Aug;162:107051. doi: 10.1016/j.compbiomed.2023.107051. Epub 2023 May 30.

引用本文的文献

1
AlphaFold, Artificial Intelligence (AI), and Allostery.AlphaFold、人工智能 (AI) 和变构。
J Phys Chem B. 2022 Sep 1;126(34):6372-6383. doi: 10.1021/acs.jpcb.2c04346. Epub 2022 Aug 17.

本文引用的文献

1
An Introductory Review of Deep Learning for Prediction Models With Big Data.大数据预测模型的深度学习入门综述
Front Artif Intell. 2020 Feb 28;3:4. doi: 10.3389/frai.2020.00004. eCollection 2020.
2
Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning.乳腺癌预后基因表达特征缺乏合理的生物学意义。
Sci Rep. 2021 Jan 8;11(1):156. doi: 10.1038/s41598-020-79375-y.
3
Named Entity Recognition and Relation Detection for Biomedical Information Extraction.用于生物医学信息提取的命名实体识别与关系检测
Front Cell Dev Biol. 2020 Aug 28;8:673. doi: 10.3389/fcell.2020.00673. eCollection 2020.
4
Causability and explainability of artificial intelligence in medicine.人工智能在医学中的可归因性与可解释性。
Wiley Interdiscip Rev Data Min Knowl Discov. 2019 Jul-Aug;9(4):e1312. doi: 10.1002/widm.1312. Epub 2019 Apr 2.
5
Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study.基于大规模电子健康记录笔记对基于变换器的双向编码器表征(BERT)模型进行微调:一项实证研究。
JMIR Med Inform. 2019 Sep 12;7(3):e14830. doi: 10.2196/14830.
6
BioBERT: a pre-trained biomedical language representation model for biomedical text mining.BioBERT:一种用于生物医学文本挖掘的预训练生物医学语言表示模型。
Bioinformatics. 2020 Feb 15;36(4):1234-1240. doi: 10.1093/bioinformatics/btz682.
7
Machine Learning in Medicine.医学中的机器学习
N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259.
8
The practical implementation of artificial intelligence technologies in medicine.人工智能技术在医学中的实际应用。
Nat Med. 2019 Jan;25(1):30-36. doi: 10.1038/s41591-018-0307-0. Epub 2019 Jan 7.
9
A primer on deep learning in genomics.深度学习在基因组学中的应用简介。
Nat Genet. 2019 Jan;51(1):12-18. doi: 10.1038/s41588-018-0295-5. Epub 2018 Nov 26.
10
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.用于整合生物学和医学数据的机器学习:原理、实践与机遇
Inf Fusion. 2019 Oct;50:71-91. doi: 10.1016/j.inffus.2018.09.012. Epub 2018 Sep 21.