Chen Mingshuai, Zhang Xin, Ju Ying, Liu Qing, Ding Yijie
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
Math Biosci Eng. 2022 Sep 19;19(12):13829-13850. doi: 10.3934/mbe.2022644.
Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.
生物序列分析是生物信息学领域一项重要的基础研究工作。随着数据的爆炸式增长，机器学习方法在生物序列分析中发挥着越来越重要的作用。通过构建预测分类器，对输入的序列特征向量进行预测和评估，从大量序列信息中获取基因结构、功能和进化等知识，为研究人员开展深入研究奠定基础。目前，许多机器学习方法已应用于生物序列分析，如RNA基因识别和蛋白质二级结构预测。RNA作为一种生物序列，在基因的编码、解码、调控和表达中发挥着重要的生物学作用。目前对RNA数据的分析主要从结构和功能方面进行，包括二级结构预测、非编码RNA识别和功能位点预测。假尿苷（Ψ）是分布最广泛、含量最丰富的RNA修饰，已在多种RNA中被发现。准确识别RNA序列中的Ψ位点对于相关功能机制研究和疾病诊断至关重要。目前，已有多种计算方法被提出作为检测Ψ位点的实验方法的替代方法，但其性能仍有提升空间。在本研究中，我们提出了一种基于孪生支持向量机（TWSVM）的Ψ位点识别模型。该模型结合了多种特征表示技术，并采用最大相关最小冗余方法获取用于训练的最优特征子集。与当前先进的Ψ位点预测器相比，独立测试准确率提高了3.4%。结果表明，我们的模型具有更好的泛化性能，提高了Ψ位点识别的准确性。iPseU-TWSVM可以成为识别Ψ位点的有用工具。
