Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases

仿形(计算机编程) 计算生物学 质谱法 生物 化学 计算机科学 色谱法 操作系统
作者
Jingjing Liu,Chaohui Bao,Jiaxin Zhang,Ze‐Guang Han,Hai Fang,Haitao Lu
出处
期刊:Pharmacology & Therapeutics [Elsevier BV]
卷期号:263: 108712-108712 被引量:10
标识
DOI:10.1016/j.pharmthera.2024.108712
摘要

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
耍酷的醉蝶完成签到 ,获得积分10
1秒前
桐桐应助土豆采纳,获得10
1秒前
2秒前
张耀发布了新的文献求助10
2秒前
3秒前
雪流星发布了新的文献求助50
3秒前
啾啾完成签到,获得积分20
3秒前
4秒前
arsheng完成签到,获得积分10
4秒前
思源应助晋启轩采纳,获得10
4秒前
4秒前
繁荣的幻梅完成签到,获得积分10
4秒前
绘空事发布了新的文献求助10
4秒前
superxiao发布了新的文献求助10
5秒前
5秒前
5秒前
6秒前
rrr应助HC采纳,获得10
6秒前
ada完成签到,获得积分10
7秒前
7秒前
Yangpc发布了新的文献求助10
7秒前
v0id应助ping采纳,获得20
8秒前
8秒前
misha发布了新的文献求助10
8秒前
共享精神应助闻风听雨采纳,获得10
8秒前
华仔应助Vino采纳,获得10
8秒前
lululiya发布了新的文献求助10
9秒前
9秒前
科研通AI6.2应助伏玉采纳,获得10
9秒前
sharony发布了新的文献求助10
10秒前
10秒前
Orange应助活泼的妙梦采纳,获得30
10秒前
10秒前
乐观化蛹发布了新的文献求助10
10秒前
菌菌完成签到,获得积分10
11秒前
云海完成签到,获得积分10
12秒前
12秒前
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
HYDROLYSE ACIDE DE QUELQUES DIOXASPIROCYCLANES 1000
Navigating Normative Orders. Interdisciplinary Perspectives 800
1 Peter and Christ's Descent to the Dead in Its Early Christian Reception 700
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 600
Organizational Behavior 510
Management and the Arts 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7740905
求助须知:如何正确求助?哪些是违规求助? 9289399
关于积分的说明 20195846
捐赠科研通 7319073
什么是DOI,文献DOI怎么找? 3306538
关于科研通互助平台的介绍 2458853
邀请新用户注册赠送积分活动 2316842