基于多特征深度迁移学习的 DNA 结合蛋白预测

IF 2.4 3区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Chunliang Wang, Fanfan kong, Yu Wang, Hongjie Wu, Jun Yan
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引用次数: 0

摘要

背景:近年来,深度学习技术的快速发展对 DNA 结合蛋白的预测产生了重大影响。深度神经网络可以自动学习蛋白质和 DNA 序列中的复杂特征,提高预测的准确性和泛化能力。目的:本文主要建立元迁移模型,并将其与深度学习模型相结合,预测DNA结合蛋白。方法:本研究引入了一种基于迁移学习的元学习算法,有助于实现快速学习和适应新任务。此外,归一化莫罗-布罗托自相关属性(NMBAC)、特定位置评分矩阵-离散余弦变换(PSSMDCT)和特定位置评分矩阵-离散小波变换(PSSM-DWT)也被用于特征提取。最后,通过基于注意力机制的深度神经网络模型实现对 DBP 的预测。结果本文首先建立了深度元转移学习的基础,并以 PDB186 数据集为基准,分别使用 NMBAC、PSSM-DCT 和 PSSM-DWT 提取特征,并对融合特征进行成对比较,最终得到融合特征过程。通过深度学习处理,得出融合特征预测效果最好的结论。同时,与目前流行的模型相比,在ACC、MCC、SN和Spec评价指标上都有明显改善。结论最后得出结论,本文所采用的方法能有效预测 DNA 结合蛋白,并表现出较为显著的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DNA Binding Protein Prediction based on Multi-feature Deep Metatransfer Learning
Background: In recent years, the rapid development of deep learning technology has had a significant impact on the prediction of DNA-binding proteins. Deep neural networks can automatically learn complex features in protein and DNA sequences, improving prediction accuracy and generalization capabilities. Objective: This article mainly establishes a meta-migration model and combines it with a deep learning model to predict DNA-binding proteins. Methods: This study introduces a meta-learning algorithm based on transfer learning, which helps achieve rapid learning and adaptation to new tasks. In addition, normalized Moreau-Broto autocorrelation attributes (NMBAC), position-specific scoring matrix-discrete cosine transform (PSSMDCT), and position-specific scoring matrix-discrete wavelet transform (PSSM-DWT) are also used for feature extraction. Finally, the prediction of DBP is achieved through the deep neural network model based on the attention mechanism. Results: This paper first establishes the basis of deep meta-transfer learning and uses the PDB186 data set as the benchmark to extract features using NMBAC, PSSM-DCT, and PSSM-DWT, respectively, and compare the fused features in pairs, and finally obtain the fused feature process. Through deep learning processing, it is concluded that the fused feature prediction effect is the best. At the same time, compared with the currently popular models, there are obvious improvements in the ACC, MCC, SN and Spec evaluation indicators. Conclusion: Finally, it was concluded that the method used in this article can effectively predict DNA-binding proteins and show more significant performance.
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来源期刊
Current Bioinformatics
Current Bioinformatics 生物-生化研究方法
CiteScore
6.60
自引率
2.50%
发文量
77
审稿时长
>12 weeks
期刊介绍: Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
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