PRFold-TNN: Protein Fold Recognition With an Ensemble Feature Selection Method Using PageRank Algorithm Based on Transformer.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-06-14 DOI:10.1109/TCBB.2024.3414497
Xinyi Qin, Lu Zhang, Min Liu, Guangzhong Liu
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Abstract

Understanding the tertiary structures of proteins is of great benefit to function in many aspects of human life. Protein fold recognition is a vital and salient means to know protein structure. Until now, researchers have successively proposed a variety of methods to realize protein fold recognition, but the novel and effective computational method is still needed to handle this problem with the continuous updating of protein structure databases. In this study, we develop a new protein structure dataset named AT and propose the PRFold-TNN model for protein fold recognition. Firstly, different types of feature extraction methods including AAC, HMM, HMM-Bigram and ACC are selected to extract corresponding features for protein sequences. Then an ensemble feature selection method based on PageRank algorithm integrating various tree-based algorithms is used to screen the fusion features. Ultimately, the classifier based on the Transformer model achieves the final prediction. Experiments show that the prediction accuracy is 86.27% on the AT dataset and 88.91% on the independent test set, indicating that the model can demonstrate superior performance and generalization ability in the problem of protein fold recognition. Furthermore, we also carry out research on the DD, EDD and TG benchmark datasets, and make them achieve prediction accuracy of 88.41%, 97.91% and 95.16%, which are at least 3.0%, 0.8% and 2.5% higher than those of the state-of-the-art methods. It can be concluded that the PRFold-TNN model is more prominent.

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PRFold-TNN:使用基于变换器的 PageRank 算法的集合特征选择方法识别蛋白质折叠。
了解蛋白质的三级结构对人类生活中许多方面的功能都大有裨益。蛋白质折叠识别是了解蛋白质结构的重要手段。迄今为止,研究人员已经相继提出了多种实现蛋白质折叠识别的方法,但随着蛋白质结构数据库的不断更新,仍然需要新颖有效的计算方法来处理这一问题。在本研究中,我们建立了一个名为 AT 的新蛋白质结构数据集,并提出了用于蛋白质折叠识别的 PRFold-TNN 模型。首先,我们选择了不同类型的特征提取方法,包括 AAC、HMM、HMM-Bigram 和 ACC,以提取蛋白质序列的相应特征。然后,使用基于 PageRank 算法的集合特征选择方法来筛选融合特征。最终,基于 Transformer 模型的分类器实现了最终预测。实验结果表明,该模型在 AT 数据集上的预测准确率为 86.27%,在独立测试集上的预测准确率为 88.91%,表明该模型在蛋白质折叠识别问题上表现出了卓越的性能和泛化能力。此外,我们还对 DD、EDD 和 TG 基准数据集进行了研究,使它们的预测准确率分别达到 88.41%、97.91% 和 95.16%,比最先进方法的预测准确率至少高出 3.0%、0.8% 和 2.5%。由此可见,PRFold-TNN 模型的优势更为突出。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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