基础模型出现时的分子因果关系

Sebastian Lobentanzer, Pablo Rodriguez-Mier, Stefan Bauer, Julio Saez-Rodriguez
{"title":"基础模型出现时的分子因果关系","authors":"Sebastian Lobentanzer, Pablo Rodriguez-Mier, Stefan Bauer, Julio Saez-Rodriguez","doi":"arxiv-2401.09558","DOIUrl":null,"url":null,"abstract":"Correlation is not causation. As simple as this widely agreed-upon statement\nmay seem, scientifically defining causality and using it to drive our modern\nbiomedical research is immensely challenging. In this perspective, we attempt\nto synergise the partly disparate fields of systems biology, causal reasoning,\nand machine learning, to inform future approaches in the field of systems\nbiology and molecular networks.","PeriodicalId":501325,"journal":{"name":"arXiv - QuanBio - Molecular Networks","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Molecular causality in the advent of foundation models\",\"authors\":\"Sebastian Lobentanzer, Pablo Rodriguez-Mier, Stefan Bauer, Julio Saez-Rodriguez\",\"doi\":\"arxiv-2401.09558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Correlation is not causation. As simple as this widely agreed-upon statement\\nmay seem, scientifically defining causality and using it to drive our modern\\nbiomedical research is immensely challenging. In this perspective, we attempt\\nto synergise the partly disparate fields of systems biology, causal reasoning,\\nand machine learning, to inform future approaches in the field of systems\\nbiology and molecular networks.\",\"PeriodicalId\":501325,\"journal\":{\"name\":\"arXiv - QuanBio - Molecular Networks\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Molecular Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2401.09558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Molecular Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2401.09558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

相关性不是因果关系。尽管这一广为认同的说法看似简单,但科学地定义因果关系并用它来推动现代生物医学研究却极具挑战性。在这一视角中,我们试图将系统生物学、因果推理和机器学习这几个互不相关的领域协同起来,为系统生物学和分子网络领域未来的研究方法提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Molecular causality in the advent of foundation models
Correlation is not causation. As simple as this widely agreed-upon statement may seem, scientifically defining causality and using it to drive our modern biomedical research is immensely challenging. In this perspective, we attempt to synergise the partly disparate fields of systems biology, causal reasoning, and machine learning, to inform future approaches in the field of systems biology and molecular networks.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信