摘要
肿瘤是影响人类健康的严重疾病,早期诊断对提高治疗成功率和患者生存率至关重要。肿瘤基因表达数据的研究已经成为揭示肿瘤疾病机制的主要工具,人工智能在肿瘤基因表达数据分析中扮演着重要角色。本文从机器学习方法的角度,探讨监督式学习、无监督式学习和深度学习在肿瘤预测和分类中的潜在优势,特别关注特征选择算法对基因筛选的影响及其在高维度基因表达数据中的重要性。通过全面综述人工智能在肿瘤基因表达数据分析中的应用与发展,旨在为未来的研究方向提供参考,促进进一步发展。
Tumors are serious diseases threatening human health,and the early diagnosis is essential to improve treatment success and patient survival.The study of tumor gene expression data has become a major tool for revealing tumor disease mechanisms,in which artificial intelligence plays an important role.The potential advantages of supervised learning,unsupervised learning and deep learning in tumor prediction and classification are explored from the perspective of machine learning methods.Special attention is paid to the impact of feature selection algorithms on gene screening and their importance in high-dimensional gene expression data.By providing a comprehensive overview of the application and development of artificial intelligence in the analysis of tumor gene expression data,the study aims to provide an outlook for future research directions and promote further development.
作者
李坤鹏
王泽朋
周玉
李四海
LI Kunpeng;WANG Zepeng;ZHOU Yu;LI Sihai(School of Information Engineering,Gansu University of Chinese Medicine,Lanzhou 730000,China)
出处
《中国医学物理学杂志》
CSCD
2024年第3期389-396,共8页
Chinese Journal of Medical Physics
基金
甘肃省科技计划项目(21JR1RA272)
甘肃省教育厅-高校教师创新基金(2023B-105)
甘肃省自然科学基金(22JR5RA606)。
关键词
基因表达数据
人工智能
机器学习
特征选择
综述
gene expression data
artificial intelligence
machine learning
feature selection
review
