基于混合特征的特征选择神经肽预测模型

doi:10.13229/j.cnki.jdxbgxb.20220007

摘要/Abstract

摘要：

提出了一种神经肽预测集成算法。整合了9个特征描述符与5个机器学习算法，生成了45个基线学习模型。第一层对这45个基线模型进行特征选择；第二层根据基线模型对的准确度和皮尔森（Pearson）相关系数之和选择8个基本学习模型；第三层将这些学习者的输出输入到逻辑回归，极限梯度提升等分类器中进行最后一步的选择用以训练最终模型，并将输出作为最终预测结果。在测试数据集上的准确度为0.9169，高于现有的模型。

关键词: 计算机应用技术, 神经肽, 特征选择, 机器学习, 堆叠方法

Abstract:

This study proposed an integrated neuropeptide prediction algorithm. This study integrated nine feature descriptors and five machine learning algorithms in order to generate 45 baseline learning models for predictive training of neuropeptides. The first layer performs feature selection on these 45 baseline models to select the features with good performance. While the second layer selects eight basic learning models based on the accuracy of the baseline model and the sum of Pearson correlation coefficients. The third layer inputs the output of these learners into logical regression， Extreme Gradient Boosting （XGBoost）， and other classifiers for the final step selection to train the final model， and uses the output as the final prediction result. The final accuracy on the test dataset is 0.9169， which is higher than existing models.

Key words: computer application technology, neuropeptide, feature selection, machine learning, stacking method

中图分类号:

TP399

周丰丰,颜振炜. 基于混合特征的特征选择神经肽预测模型[J]. 吉林大学学报(工学版), 2023, 53(11): 3238-3245.

Feng-feng ZHOU,Zhen-wei YAN. A model for identifying neuropeptides by feature selection based on hybrid features[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3238-3245.

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特征策略	特征构造方法名称	缩写	向量维度
基于合成的特征	氨基酸组成	AAC	60
	二肽组成	DPC	400
基于二元图谱的特征	二元图谱特征	BPNC	200
	氨基酸索引特征	AAI	36
	分组氨基酸组成	GAAC	15
基于理化性质的特征	分组二肽组成	GDPC	25
	分组三肽组成	GTPC	125
	成分-过渡-分布	CTD	147
基于位置的特征	氨基酸熵	AAE	60

方法	MCC	ACC	AUC	PRAUC
LR	0.7938	0.8969	0.9543	0.9626
ANN	0.7917	0.8958	0.9536	0.9619
ERT	0.7958	0.8979	0.9520	0.9610
KNN	0.7796	0.8896	0.9339	0.9434
XGBoost	0.7939	0.9064	0.9500	0.9676

方法	ACC
方差选择	0.8986
相关系数	0.9075
距离相关系数	0.9093
卡方检验	0.9083
互信息	0.9087
递归特征消除	0.9094
L1正则化/Lasso	0.9104
L2正则化/Ridge regression	0.9126
RF	0.9169
Relief	0.9149
GBDT	0.9138
PCA	0.9115
LDA	0.9129

方法	ACC	MCC	AUC
本文	0.9169	0.9010	0.9770
PredNeuroP	0.8969	0.7940	0.9540
NeuroPIpred	0.5360	0.0740	0，5810