Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease.
Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease.
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DOI：
10.1093/ofid/ofab483
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发表时间：
2021-11
影响因子：
4.2
通讯作者：
Vyas JM
中科院分区：
文献类型：
--
作者：
Ward RA;Aghaeepour N;Bhattacharyya RP;Clish CB;Gaudillière B;Hacohen N;Mansour MK;Mudd PA;Pasupneti S;Presti RM;Rhee EP;Sen P;Spec A;Tam JM;Villani AC;Woolley AE;Hsu JL;Vyas JM
传染病领域目前采取反应性方法，并在患者出现感染时进行治疗。虽然已知某些人群发生感染的风险更高（例如，免疫功能低下），但我们缺乏一种系统方法来确定患者未来感染的真正风险。在“组学”技术令人印象深刻的进展的指导下，未来的传染病战略应通过确定处于中等和高风险感染的患者，对感染采取精确的方法，并部署有针对性的预防措施（即预防）。通过多组学方法（即转录组学、表观基因组学、代谢组学、蛋白质组学）进行高通量免疫分析的进展有望识别感染风险增加的患者，并使风险分层方法能够应用于临床。使用机器学习整合患者特定数据提高了预测的有效性，提供了推动传染病医学领域进入个性化医学时代所需的必要技术。传染病的转化系统免疫学方法将使患者从反应性治疗转向精确治疗，这将改善临床结果，同时减少预防性抗生素的使用和高危人群的感染发生率。
The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in “omics” technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine. Translational systems immunology approaches to infectious diseases will enable the switch from reactive to precision treatment of patients, which will improve clinical outcomes while reducing the use of prophylactic antibiotics and incidence of infection in high-risk individuals.
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影响因子：
4.6
作者：
Candia J;Cheung F;Kotliarov Y;Fantoni G;Sellers B;Griesman T;Huang J;Stuccio S;Zingone A;Ryan BM;Tsang JS;Biancotto A
通讯作者：
Biancotto A
胰腺癌患者的存活率和复发预测通过整合多词数据。
影响因子：
4.6
作者：
Baek B;Lee H
通讯作者：
Lee H
影响因子：
5.6
作者：
Chin VK;Lee TY;Rusliza B;Chong PP
通讯作者：
Chong PP
影响因子：
64.5
作者：
Bekkering, Siroon;Arts, Rob J. W.;Netea, Mihai G.
通讯作者：
Netea, Mihai G.
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP