Navigating the omics landscape in precision medicine: A bidirectional approach to patient care

Rui Vitorino
{"title":"Navigating the omics landscape in precision medicine: A bidirectional approach to patient care","authors":"Rui Vitorino","doi":"10.1016/j.oor.2024.100660","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a novel bidirectional approach to precision medicine that combines proteomic, peptidomic and metabolomic analyzes with clinical data and genome-wide association studies (GWAS). This innovative strategy improves patient care by enabling a dynamic exchange from “patient to molecular pathway” and back, which significantly refines patient cohort stratification, improves diagnostic accuracy and personalizes treatment strategies. At the heart of this approach is the use of comprehensive multi-omics data to improve patient management by tailoring interventions to individual molecular profiles. This two-way flow not only optimizes treatment based on real-time insights from molecular pathways, but also improves the application of GWAS results in clinical scenarios. Advanced computational tools such as machine learning and network analysis are critical for navigating these complex data sets and translating intricate molecular data into actionable treatment plans. This integrated, adaptive framework promises to reshape the future of healthcare by tailoring treatments to patients' unique biological and genetic profiles.</div></div>","PeriodicalId":94378,"journal":{"name":"Oral Oncology Reports","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oral Oncology Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772906024005065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents a novel bidirectional approach to precision medicine that combines proteomic, peptidomic and metabolomic analyzes with clinical data and genome-wide association studies (GWAS). This innovative strategy improves patient care by enabling a dynamic exchange from “patient to molecular pathway” and back, which significantly refines patient cohort stratification, improves diagnostic accuracy and personalizes treatment strategies. At the heart of this approach is the use of comprehensive multi-omics data to improve patient management by tailoring interventions to individual molecular profiles. This two-way flow not only optimizes treatment based on real-time insights from molecular pathways, but also improves the application of GWAS results in clinical scenarios. Advanced computational tools such as machine learning and network analysis are critical for navigating these complex data sets and translating intricate molecular data into actionable treatment plans. This integrated, adaptive framework promises to reshape the future of healthcare by tailoring treatments to patients' unique biological and genetic profiles.
在精准医疗中驾驭全局:患者护理的双向方法
本文介绍了一种新颖的精准医疗双向方法,它将蛋白质组、肽组和代谢组分析与临床数据和全基因组关联研究(GWAS)相结合。这种创新策略通过实现从 "患者到分子通路 "再到 "分子通路 "的动态交流来改善患者护理,从而显著完善患者队列分层、提高诊断准确性并个性化治疗策略。这种方法的核心是利用全面的多组学数据,根据个体分子特征调整干预措施,从而改善患者管理。这种双向流动不仅能根据分子通路的实时见解优化治疗,还能改进 GWAS 结果在临床中的应用。机器学习和网络分析等先进的计算工具对于浏览这些复杂的数据集以及将复杂的分子数据转化为可操作的治疗方案至关重要。这种集成的自适应框架有望根据患者独特的生物和遗传特征量身定制治疗方案,从而重塑未来的医疗保健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
0.20
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