基于Flex-Bootstrap与神经网络融合模型的蛋白质质谱数据分析  

Analysis of Protein Mass Spectrometry Data using Flex-Bootstrap and Neural Network Fusion Model

作　　者：张海强 李勇[1] 向诚[2] Zhang Haiqiang;Li Yong;Xiang Cheng(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650550,Yunnan,China;Faculty of Life Science and Technology,Kunming University of Science and Technology,Kunming 650550,Yunnan,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650550 [2]昆明理工大学生命科学与技术学院,云南昆明650550

出　　处：《激光与光电子学进展》2023年第16期334-339,共6页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(82160787);贵州金钗石斛与铁皮石斛化学物质基础的质量标准化研究(20025800400)。

摘　　要：针对蛋白质质谱数据检索研究中由于样本单一、数据不平衡导致传统的相似性匹配检索方法效率低且精度不高的问题,提出一种基于复杂的放回抽样(Flex-Bootstrap)和多次卷积神经网络(Multi-CNN)与深度神经网络(DNN)融合模型的检索方法,并与DNN模型、CNN与DNN融合模型相比较。Flex-Bootstrap方法结合Multi-CNN与DNN融合模型应用于蛋白质质谱数据种类预测时取得了较好的效果,其测试集的准确率提升至98.82%,损失函数值降低至0.0397。该模型不仅有效解决了使用DNN模型、CNN与DNN融合模型进行数据检索时存在的欠拟合问题,同时提高了预测的准确率以及质谱数据库的搜索效率。In this study,a retrieval method based on complex Flex-Bootstrap and multi-convolution neural network(Multi-CNN)and deep neural network(DNN)fusion model is proposed to solve the problems of low efficiency and low accuracy of traditional similarity matching retrieval methods due to single sample and unbalanced data in protein mass spectrometry data retrieval research.Here,we compared our proposed method with the DNN model,CNN,and DNN fusion model.Furthermore,the Flex-Bootstrap method combined with the Multi-CNN and DNN fusion model has achieved promising results when applied in the prediction of protein mass spectrum data types.Experimental results revealed that the test set’s accuracy was increased to 98.82%,and the loss function value was reduced to 0.0397.Therefore,this model not only effectively solves the problem of underfitting in data retrieval using the DNN model and CNN and DNN fusion model,but also enhances the accuracy of prediction and the search efficiency of the mass spectrometry database.

关 键 词：医用光学 蛋白质种类 复杂的放回抽样 深度神经网络 卷积神经网络 质谱数据预测 

分 类 号：O629.73[理学—有机化学]