Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington DC, 20007, USA.
Brief Bioinform. 2024 Jan 22;25(2). doi: 10.1093/bib/bbae098.
Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.
转化生物信息学和数据科学在生物标志物发现中起着至关重要的作用，因为它能够促进转化研究，并有助于弥合基础研究和床边临床应用之间的差距。由于更新更快的分子分析技术和成本降低，研究人员有许多机会探索疾病的分子和生理机制。生物标志物发现使研究人员能够更好地描述患者，实现早期检测和干预/预防，并预测治疗反应。由于精神健康（MH）障碍的患病率不断增加和治疗成本不断上升，它们已成为生物标志物发现的重要领域，目标是改善患者的诊断、治疗和护理。探索潜在的生物学机制是理解 MH 障碍发病机制和病理生理学的关键。为了更好地理解 MH 障碍的潜在机制，我们从生物信息学和数据科学的角度回顾了 MH 领域的主要成就，总结了来自分子和细胞数据的现有知识，并描述了该领域的挑战和机遇。
