School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
Comput Biol Med. 2023 Sep;164:107094. doi: 10.1016/j.compbiomed.2023.107094. Epub 2023 Jun 16.
In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods have been widely used in DNA-binding proteins research. The aforementioned methods are indeed more efficient than those based on manual classification, but there is still room for improvement in terms of prediction accuracy and speed. In this study, researchers used Average Blocks, Discrete Cosine Transform, Discrete Wavelet Transform, Global encoding, Normalized Moreau-Broto Autocorrelation and Pseudo position-specific scoring matrix to extract evolutionary features. A dynamic deep network based on lifelong learning architecture was then proposed in order to fuse six features and thus allow for more efficient classification of DNA-binding proteins. The multi-feature fusion allows for a more accurate description of the desired protein information than single features. This model offers a fresh perspective on the dichotomous classification problem in bioinformatics and broadens the application field of lifelong learning. The researchers ran trials on three datasets and contrasted them with other classification techniques to show the model's effectiveness in this study. The findings demonstrated that the model used in this research was superior to other approaches in terms of single-sample specificity (81.0%, 83.0%) and single-sample sensitivity (82.4%, 90.7%), and achieves high accuracy on the benchmark dataset (88.4%, 80.0%, and 76.6%).
近年来，生物信息学领域的研究集中在预测蛋白质的原始序列上，一些学者将 DNA 结合蛋白预测视为分类任务。许多基于统计和机器学习的方法已被广泛应用于 DNA 结合蛋白的研究中。上述方法确实比基于手动分类的方法更高效，但在预测准确性和速度方面仍有改进的空间。在这项研究中，研究人员使用平均块、离散余弦变换、离散小波变换、全局编码、归一化 Moreau-Broto 自相关和伪位置特异性评分矩阵来提取进化特征。然后提出了一种基于终身学习架构的动态深度网络，以融合六个特征，从而更有效地对 DNA 结合蛋白进行分类。多特征融合比单一特征更能准确地描述所需的蛋白质信息。该模型为生物信息学中的二分分类问题提供了新的视角，并拓宽了终身学习的应用领域。研究人员在三个数据集上进行了试验，并与其他分类技术进行了对比，以展示该模型在本研究中的有效性。研究结果表明，与其他方法相比，该研究中使用的模型在单一样本特异性（81.0%、83.0%）和单一样本敏感性（82.4%、90.7%）方面表现更好，在基准数据集上也实现了高准确性（88.4%、80.0%和 76.6%）。
