网络医学和人工智能在癌症精准治疗中的应用:预防药物毒副作用的途径

IF 4.6
Asim Bikas Das
{"title":"网络医学和人工智能在癌症精准治疗中的应用:预防药物毒副作用的途径","authors":"Asim Bikas Das","doi":"10.1016/j.cotox.2024.100476","DOIUrl":null,"url":null,"abstract":"<div><p>The discovery of cancer-specific therapeutics and determining their sensitivity is a critical step in preventing drug-induced toxicity. Drug sensitivity varies among cancer patients due to intra-tumor heterogeneity. It demands rational drug design, target identification, and novel treatment modalities. This review discusses the use of network medicine in targeted therapy and AI-based drug response prediction for personalized cancer therapy. The network medicine is successfully implemented to integrate multiple omics data to identify the disease modules in cancer. The cancer-specific disease modules are utilized for drug screening and targeted therapy. Additionally, the model developed using AI, and genomic data shows superior performance and also reveals relationships between the genomic variability of cancer and their response to drugs. There is significant promise for network medicine and AI to handle large-scale omics data, leading to the identification of a novel cancer-specific treatment strategy and improved patient care.</p></div>","PeriodicalId":93968,"journal":{"name":"Current opinion in toxicology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Network medicine and artificial intelligence in cancer precision therapy: Path to prevent drug-induced toxic side effect\",\"authors\":\"Asim Bikas Das\",\"doi\":\"10.1016/j.cotox.2024.100476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The discovery of cancer-specific therapeutics and determining their sensitivity is a critical step in preventing drug-induced toxicity. Drug sensitivity varies among cancer patients due to intra-tumor heterogeneity. It demands rational drug design, target identification, and novel treatment modalities. This review discusses the use of network medicine in targeted therapy and AI-based drug response prediction for personalized cancer therapy. The network medicine is successfully implemented to integrate multiple omics data to identify the disease modules in cancer. The cancer-specific disease modules are utilized for drug screening and targeted therapy. Additionally, the model developed using AI, and genomic data shows superior performance and also reveals relationships between the genomic variability of cancer and their response to drugs. There is significant promise for network medicine and AI to handle large-scale omics data, leading to the identification of a novel cancer-specific treatment strategy and improved patient care.</p></div>\",\"PeriodicalId\":93968,\"journal\":{\"name\":\"Current opinion in toxicology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current opinion in toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468202024000184\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468202024000184","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

发现癌症特异性疗法并确定其敏感性是预防药物毒性的关键一步。由于肿瘤内部的异质性,癌症患者对药物的敏感性各不相同。这就需要合理的药物设计、靶点识别和新型治疗模式。本综述讨论了网络医学在靶向治疗中的应用,以及基于人工智能的个性化癌症治疗药物反应预测。网络医学已成功应用于整合多种全息数据,以识别癌症的疾病模块。癌症特异性疾病模块可用于药物筛选和靶向治疗。此外,利用人工智能和基因组数据开发的模型显示出卓越的性能,还揭示了癌症基因组变异性与其对药物反应之间的关系。网络医学和人工智能在处理大规模 Omics 数据方面大有可为,可帮助确定新型癌症特异性治疗策略并改善患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network medicine and artificial intelligence in cancer precision therapy: Path to prevent drug-induced toxic side effect

The discovery of cancer-specific therapeutics and determining their sensitivity is a critical step in preventing drug-induced toxicity. Drug sensitivity varies among cancer patients due to intra-tumor heterogeneity. It demands rational drug design, target identification, and novel treatment modalities. This review discusses the use of network medicine in targeted therapy and AI-based drug response prediction for personalized cancer therapy. The network medicine is successfully implemented to integrate multiple omics data to identify the disease modules in cancer. The cancer-specific disease modules are utilized for drug screening and targeted therapy. Additionally, the model developed using AI, and genomic data shows superior performance and also reveals relationships between the genomic variability of cancer and their response to drugs. There is significant promise for network medicine and AI to handle large-scale omics data, leading to the identification of a novel cancer-specific treatment strategy and improved patient care.

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
Current opinion in toxicology
Current opinion in toxicology Toxicology, Biochemistry
CiteScore
8.50
自引率
0.00%
发文量
0
审稿时长
64 days
×
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