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.