机器学习在胃癌生物标志物挖掘中的应用进展  

Applicationprogress of machine learning in mining of gastric cancer biomarker

作　　者：严健亮 景蓉蓉[1] 谢泽宇 崔明[1] YAN Jianliang;JING Rongrong;XIE Zeyu;CUI Ming(Department of Laboratory Medicine,Affiliated Hospital of Nantong University,Medical College of Nantong University,Nantong 226006,China)

机构地区：[1]南通大学附属医院检验科,江苏南通226006 [2]南通大学医学院,江苏南通226006 [3]河海大学商学院,南京211100

出　　处：《实用医学杂志》2023年第6期783-787,共5页The Journal of Practical Medicine

基　　金：中国博士后科学基金资助项目(编号:2020M681688);南通市市级基础科学研究和社会民生科技指令性项目(编号:MS12021001)。

摘　　要：胃癌是全球最常见的消化道恶性肿瘤之一。随着科技的发展,大量胃癌相关的生物标志物被挖掘并应用于临床。近年来,机器学习因其高效的特征发现和学习推理能力在胃癌生物标志物研究领域得到广泛应用。本文综合归纳了医学标志物挖掘中常用机器学习算法的特征,分析其在胃癌诊断、疗效监测及预后判断相关生物标志物挖掘中的应用价值,并对今后的研究方向进行展望。Gastric cancer is one of the most common malignant tumors of digestive tract in the world.With the development of science and technology,a large number of gastric cancer biomarkers have been excavated and applied to clinical practice.Recently,machine learning has been widely used in the research of gastric cancer biomarkers because of its efficient feature discovery and learning reasoning ability.This review summarizes the characteristics of commonly used machine learning algorithms in biomarker mining,analyzes the application value of biomarker mining in the diagnosis,efficacy monitoring and prognosis of gastric cancer and estimates the future research direction.

关 键 词：胃癌 机器学习 生物标志物 数据挖掘 

分 类 号：R319[医药卫生—基础医学]