数字组学--数字转型带来全息洞察。

IF 3.8 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Expert Review of Proteomics Pub Date : 2024-09-01 Epub Date: 2024-10-11 DOI:10.1080/14789450.2024.2413107
Nandha Kumar Balasubramaniam, Scott Penberthy, David Fenyo, Nina Viessmann, Christoph Russmann, Christoph H Borchers
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引用次数: 0

摘要

导言:生物标记物的发现正日益从单一组学转向多组学,以及从多细胞组学转向单细胞组学。这些转变越来越多地采用数字化转型技术,以加快从数据到洞察力的进程。在这里,我们将讨论 "数字组学 "的概念,以及数字化转型如何直接影响生物标记物的发现。这将最终帮助临床医生做出个性化治疗和精准医疗的治疗决策:基于基因型到表型的洞察力生成涉及整合大量复杂的多组学数据。这种数据整合和分析可通过数字化转型来实现,从而带来更好的临床结果。我们还强调了数字组学的挑战和机遇,并举例说明了人工智能、云计算和高性能计算的应用,以及在多组学分析工作流中使用张量:在数字化转型的帮助下,生物标记物的发现正在对癌症、心血管疾病、传染病、免疫疾病和神经系统疾病等产生重大影响。从多组学分析中获得的数据洞察力与患者元数据相结合,有助于对患者进行分层,并对各种疾病进行有针对性的治疗。数字化转型可节省时间和成本,同时改善专利医疗服务。在此,我们将重点介绍数字化转型对基于多组学的生物标记物发现的影响,以及与肿瘤学相关的具体应用。
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Digitalomics - digital transformation leading to omics insights.

Introduction: Biomarker discovery is increasingly moving from single omics to multiomics, as well as from multi-cell omics to single-cell omics. These transitions have increasingly adopted digital transformation technologies to accelerate the progression from data to insight. Here, we will discuss the concept of 'digitalomics' and how digital transformation directly impacts biomarker discovery. This will ultimately assist clinicians in personalized therapy and precision-medicine treatment decisions.

Areas covered: Genotype-to-phenotype-based insight generation involves integrating large amounts of complex multiomic data. This data integration and analysis is aided through digital transformation, leading to better clinical outcomes. We also highlight the challenges and opportunities of Digitalomics, and provide examples of the application of Artificial Intelligence, cloud- and high-performance computing, and use of tensors for multiomic analysis workflows.

Expert opinion: Biomarker discovery, aided by digital transformation, is having a significant impact on cancer, cardiovascular, infectious, immunological, and neurological diseases, among others. Data insights garnered from multiomic analyses, combined with patient meta data, aids patient stratification and targeted treatment across a broad spectrum of diseases. Digital transformation offers time and cost savings while leading to improved patent healthcare. Here, we highlight the impact of digital transformation on multiomics- based biomarker discovery with specific applications related to oncology.

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来源期刊
Expert Review of Proteomics
Expert Review of Proteomics 生物-生化研究方法
CiteScore
7.60
自引率
0.00%
发文量
20
审稿时长
6-12 weeks
期刊介绍: Expert Review of Proteomics (ISSN 1478-9450) seeks to collect together technologies, methods and discoveries from the field of proteomics to advance scientific understanding of the many varied roles protein expression plays in human health and disease. The journal coverage includes, but is not limited to, overviews of specific technological advances in the development of protein arrays, interaction maps, data archives and biological assays, performance of new technologies and prospects for future drug discovery. The journal adopts the unique Expert Review article format, offering a complete overview of current thinking in a key technology area, research or clinical practice, augmented by the following sections: Expert Opinion - a personal view on the most effective or promising strategies and a clear perspective of future prospects within a realistic timescale Article highlights - an executive summary cutting to the author''s most critical points.
期刊最新文献
Proteomic insights into the extracellular matrix: a focus on proteoforms and their implications in health and disease. Validating proteomic biomarkers in saliva: distinguishing between health and periodontal diseases. Proteomic profiling of oral squamous cell carcinoma tissues altered-related proteins: implications for personalized therapy. Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers. Cellular thermal shift assay: an approach to identify and assess protein target engagement.
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