基因调控网络 (GRN) 有助于了解基因的功能和癌症的发展或关键基因对疾病的影响。因此，本研究提出了一种基于13种基本分类方法和灵活神经树（FNT）的集成方法来提高GRN识别精度。主要分类方法包括岭分类、随机梯度下降、高斯过程分类、伯努利朴素贝叶斯、自适应提升、梯度提升决策树、直方图梯度提升分类、极限梯度提升（XGBoost）、多层感知器、光梯度提升机、随机森林、支持向量机、k近邻算法作为FNT模型的输入变量集。此外，还开发了一种基于基因编程变体和粒子群优化的混合进化算法来搜索最佳 FNT 模型。对三个模拟数据集和三个真实单细胞 RNA-seq 数据集的实验表明，所提出的集成特征优于 13 种监督算法、7 种无监督算法（ARACNE、CLR、GENIE3、MRNET、PCACMI、GENECI 和 EPCACMI）和 4 种单细胞算法基于接收者操作特征曲线下面积、精确回忆曲线下面积和 F1 指标的特定方法（SCODE、BiRGRN、LEAP 和 BiGBoost）。© 作者 2024 年。由牛津大学出版社出版。版权所有。如需权限，请发送电子邮件至：journals.permissions@oup.com。

Gene regulatory networks (GRNs) contribute toward understanding the function of genes and the development of cancer or the impact of key genes on diseases. Hence, this study proposes an ensemble method based on 13 basic classification methods and a flexible neural tree (FNT) to improve GRN identification accuracy. The primary classification methods contain ridge classification, stochastic gradient descent, Gaussian process classification, Bernoulli Naive Bayes, adaptive boosting, gradient boosting decision tree, hist gradient boosting classification, eXtreme gradient boosting (XGBoost), multilayer perceptron, light gradient boosting machine, random forest, support vector machine, and k-nearest neighbor algorithm, which are regarded as the input variable set of FNT model. Additionally, a hybrid evolutionary algorithm based on a gene programming variant and particle swarm optimization is developed to search for the optimal FNT model. Experiments on three simulation datasets and three real single-cell RNA-seq datasets demonstrate that the proposed ensemble feature outperforms 13 supervised algorithms, seven unsupervised algorithms (ARACNE, CLR, GENIE3, MRNET, PCACMI, GENECI, and EPCACMI) and four single cell-specific methods (SCODE, BiRGRN, LEAP, and BiGBoost) based on the area under the receiver operating characteristic curve, area under the precision-recall curve, and F1 metrics.© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.