Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning.
Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning.
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DOI：
10.3390/genes12050722
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发表时间：
2021-05-12
期刊：
影响因子：
3.5
通讯作者：
Kaneko G
中科院分区：
文献类型：
--
作者：
Baptiste M;Moinuddeen SS;Soliz CL;Ehsan H;Kaneko G
精准医疗是一种医学方法，通过考虑到一个人的基因、环境和生活方式的可变性，为病人提供量身定制的治疗剂量。组学大序列数据的积累导致了各种遗传数据库的发展，这些数据库可用于高危人群的临床分层。此外，由于癌症通常是由肿瘤特异性突变引起的，各种肿瘤中单核苷酸多态性（snp）的大规模系统鉴定推动了肿瘤定制治疗（即精确肿瘤学）的重大进展。机器学习（ML）是人工智能的一个子领域，计算机通过经验学习，在精确肿瘤学中有很大的潜力，主要是帮助医生根据肿瘤图像做出诊断决定。从图像到多组学大数据，整合所有可用数据，用于患者和高风险健康受试者的整体护理，是ML在精确肿瘤学领域的一个有前景的领域。在这篇综述中，我们提供了精确肿瘤学和机器学习的重点概述，重点是乳腺癌和胶质瘤，以及具有灵活性和处理不完整信息的能力的贝叶斯网络。我们还介绍了一些最先进的尝试使用和结合ML和遗传信息在精确肿瘤学。
Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.
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