Application of Machine Learning in Genomic Data Analysis of Cerebrovascular Diseases

doi:10.3969/j.issn.1673－5765.2023.07.003

Abstract

Abstract: With the advent of the era of precision medicine, genomics research is gradually gaining widespread attention in the field of cerebrovascular diseases. Due to the high-dimensional and complexity of genomics data, machine learning is now an effective tool for analyzing genomics data. This article introduced the basic concepts of machine learning, the main steps, the classification of algorithms and the application of each algorithm of machine learning in genomics research in the field of cerebrovascular diseases, in order to provide a reference for future genomics research in cerebrovascular diseases.

Key words: Cerebrovascular disease; Machine learning; Deep learning; Genomics

摘要： 随着精准医疗时代的到来，在脑血管病领域，基因组学研究受到越来越多的关注。基因组学数据的高维复杂性，使得机器学习成为分析基因组学数据的最为有效的工具之一。本文对机器学习的基本概念、主要步骤、算法分类以及各算法在脑血管病领域基因组学研究中的应用现状进行介绍，以期为未来脑血管病基因组学研究提供参考。

关键词: 脑血管病; 机器学习; 深度学习; 基因组学

JIANG Yingyu, CHEN Siding, QIU Xin, GU Hongqiu. Application of Machine Learning in Genomic Data Analysis of Cerebrovascular Diseases [J]. Chinese Journal of Stroke, 2023, 18(07): 751-757.

姜英玉, 陈思玎, 仇鑫, 谷鸿秋. 机器学习在脑血管病基因组学数据分析中的应用进展[J]. 中国卒中杂志, 2023, 18(07): 751-757.

References

[1] GBD 2016 Causes of Death Collaborators. Global，regional，and national age-sex specific mortality for 264 causes of death，1980-2016：a systematic analysis for the global burden of disease study 2016[J]. Lancet，2017，390（10100）：1151-1210.
[2] WANG Y J，LI Z X，GU H Q，et al. China stroke statistics 2019：a report from the National Center for Healthcare Quality Management in Neurological Diseases，China National Clinical Research Center for Neurological Diseases，the Chinese Stroke Association，National Center for Chronic and Non-communicable Disease Control and Prevention，Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations[J]. Stroke Vasc Neurol，2020，5（3）：211-239.
[3] MARKUS H S，BEVAN S. Mechanisms and treatment of ischaemic stroke-insights from genetic associations[J]. Nat Rev Neurol，2014，10（12）：723-730.
[4] CAMPBELL B C V，DE SILVA D A，MACLEOD M R，et al. Ischaemic stroke[J/OL]. Nat Rev Dis Primers，2019，5（1）：70[2023-05-10]. https://doi.org/10.1038/s41572-019-0118-8.
[5] JIANG Y H，YANG M，WANG S H，et al. Emerging role of deep learning-based artificial intelligence in tumor pathology[J]. Cancer Commun（Lond），2020，40（4）：154-166.
[6] JIANG F，JIANG Y，ZHI H，et al. Artificial intelligence in healthcare：past，present and future[J]. Stroke Vasc Neurol，2017，2（4）：230-243.
[7] LIU H J，HOU L P，XU S H，et al. Discovering cerebral ischemic stroke associated genes based on network representation learning[J/OL]. Front Genet，2021，12：728333[2023-05-10]. https://doi.org/10.3389/fgene.2021.728333.
[8] THEOFILATOS K，KORFIATI A，MAVROUDI S，et al. Discovery of stroke-related blood biomarkers from gene expression network models[J/OL]. BMC Med Genomics，2019，12（1）：118[2023-05-10]. https://doi.org/10.1186/s12920-019-0566-8.
[9] MARTHA S R，CHENG Q，FRASER J F，et al. Expression of cytokines and chemokines as predictors of stroke outcomes in acute ischemic stroke[J/OL]. Front Neurol，2020，10：1391[2023-05-10]. https://doi.org/10.3389/fneur.2019.01391.
[10] ROYSTON P，MOONS K G，ALTMAN D G，et al. Prognosis and prognostic research：developing a prognostic model[J/OL]. BMJ，2009，338：b604[2023-05-10]. https://doi.org/10.1136/bmj.b604
[11] WANG X，WANG Q，WANG K，et al. Is immune suppression involved in the ischemic stroke? A study based on computational biology[J/OL]. Front Aging Neurosci，2022，14：830494[2023-05-10]. https://doi.org/10.3389/fnagi.2022.830494.
[12] O'CONNELL G C，PETRONE A B，TREADWAY M B，et al. Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke[J/OL]. NPJ Genom Med，2016，1：16038[2023-05-10]. https://doi.org/10.1038/npjgenmed.2016.38.
[13] WANG X，MENG X Y，MENG L B，et al. Joint efficacy of the three biomarkers SNCA，GYPB and HBG1 for atrial fibrillation and stroke：analysis via the support vector machine neural network[J]. J Cell Mol Med，2022，26（7）：2010-2022.
[14] ERASLAN G，AVSEC Ž，GAGNEUR J，et al. Deep learning：new computational modelling techniques for genomics[J]. Nat Rev Genet，2019，20（7）：389-403.
[15] GREENER J G，KANDATHIL S M，MOFFAT L，et al. A guide to machine learning for biologists[J]. Nat Rev Mol Cell Biol，2022，23（1）：40-55.
[16] NEWMAN A M，LIU C L，GREEN M R，et al. Robust enumeration of cell subsets from tissue expression profiles[J]. Nat Methods，2015，12（5）：453-457.
[17] WHALEN S，SCHREIBER J，NOBLE W S，et al. Navigating the pitfalls of applying machine learning in genomics[J]. Nat Rev Genet，2022，23（3）：169-181.
[18] 邱越，邢卓，孙筱松，等. 机器学习在多组学数据分析中的应用[J]. 科技与创新，2021，8（24）：170-171.