实验科学与技术2025，Vol.23Issue(2)：84-89,6.DOI:10.12179/1672-4550.20230583
基于机器学习的衰老基因特征选择与分类
Machine Learning-Based Aging Gene Feature Selection and Classification
摘要
Abstract
A machine learning-based aging gene feature selection and classification experiment is designed as the experimental content of the"Machine Learning Basics"course for intelligent medical engineering and other majors.In this experiment,the data set is obtained by mapping aging genes to gene ontology,feature selection methods are used to deal with feature redundancy in gene ontology,and classification models such as naive Bayesian and support vector machines are used to classify aging genes.The experiment is implemented with Python language and Scikit-learn framework.In addition to the built-in methods of the framework,a hierarchical feature selection method based on the statistical properties of the data and the uniqueness of the test sample is designed to eliminate the hierarchical redundancy among features.Experimental results show that effective feature selection methods can significantly improve the results of aging gene classification.关键词
特征选择/分类/机器学习/衰老基因/基因本体Key words
feature selection/classification/machine learning/aging genes/gene ontology分类
信息技术与安全科学引用本文复制引用
曾洁,吴全旺,李德辉,高俊敏..基于机器学习的衰老基因特征选择与分类[J].实验科学与技术,2025,23(2):84-89,6.基金项目
国家自然科学基金面上项目(62172065) （62172065）
重庆市教改项目(213029) （213029）
重庆大学教改项目(2021Y34). （2021Y34）
