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PD-BertEDL: an Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability

International Journal of Molecular Sciences(2022)

Taiyuan Univ Technol

Cited 2|Views16
Abstract
Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful for disease treatment and clinical research. However, most existing computational methods for predicting peptide detectability rely on a single information. With the increasing complexity of feature representation, it is necessary to explore the influence of multivariate information on peptide detectability. Thus, we propose an ensemble deep learning method, PD-BertEDL. Bidirectional encoder representations from transformers (BERT) is introduced to capture the context information of peptides. Context information, sequence information, and physicochemical information of peptides were combined to construct the multivariate feature space of peptides. We use different deep learning methods to capture the high-quality features of different categories of peptides information and use the average fusion strategy to integrate three model prediction results to solve the heterogeneity problem and to enhance the robustness and adaptability of the model. The experimental results show that PD-BertEDL is superior to the existing prediction methods, which can effectively predict peptide detectability and provide strong support for protein identification and quantitative analysis, as well as disease treatment.
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Key words
peptide detectability,BERT,multivariate representation,ensemble deep learning
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要点】:本文提出了PD-BertEDL方法,一种结合BERT和多元表征的集成深度学习模型,有效预测肽段的检测性,提升蛋白质识别和分析的准确性。

方法】:利用BERT捕获肽段上下文信息,并结合序列信息、物理化学信息构建肽段的多元特征空间,通过不同深度学习模型提取各类信息的高质量特征,并采用平均融合策略整合模型预测结果。

实验】:在实验中,模型使用标准样本混合物的数据集进行训练和测试,结果显示PD-BertEDL方法在预测肽段检测性上优于现有方法。