Munger Eric, Hickey John W, Dey Amit K, Jafri Mohsin Saleet, Kinser Jason M, Mehta Nehal N
Stanford University, Stanford, California 94306, USA.
National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
APL Bioeng. 2021 Feb 16;5(1):011505. doi: 10.1063/5.0028986. eCollection 2021 Mar.
Biological processes are incredibly complex-integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.
生物过程极其复杂，它整合了参与多细胞通讯和功能的分子信号网络，从而维持体内平衡。这些过程的功能障碍会导致体内平衡的破坏，引发包括动脉粥样硬化在内的多种疾病进程。我们对动脉粥样硬化中的生物过程有了显著深入的理解，在此过程中，我们开始认识到动脉粥样硬化的复杂性、错综性和异质性。我们现在也更有能力获取、存储和处理揭示相关生物回路所需的大量生物数据。这些数据可以在机器学习框架内进行分析，以更好地梳理出此类复杂关系。事实上，越来越多的研究将机器学习方法应用于基于合并症的患者风险分层、多模态图像处理以及与动脉粥样硬化斑块形成相关的生物标志物发现。在此，我们专注于机器学习的当前应用，以深入了解动脉粥样硬化斑块的形成，并更好地理解心血管疾病患者的动脉粥样硬化斑块进展情况。
