A DNA-Binding Proteins Prediction Model Using Different Property Distance Transformation

Xiangyu Li, Lina Yang, Y. Tang, P. Wang
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Abstract

DNA-binding proteins refers to a class of proteins that can combine with DNA to produce complexes. It is an indispensable part of cell life activities, such as DNA recombination, modification, replication, virus integration and transcription. With the rapid development of gene sequencing technology and the increasing demand for sequencing technology, more and more unknown DNA-binding proteins are waiting for researchers to predict. However, develop a high quality and short time prediction method still face more challenges. In this article the author puts forward a new method named PSFM-DDT, which combines the Position Specific Frequency Matrix(PSFM) and Different Property Distance Transformation(DDT). Firstly, the evolutionary information of protein sequence was expressed by frequency matrix, and then using distance transformation of different amino acids is transformed into a series of new feature vector. The extracted vectors features are trained by using Support Vector Machine(SVM) linear kernel method to choice the last model. The accuracy of this method reached 83.16% using jackknife test on the benchmark dataset and 79.57% on the independent dataset. Through the experimental results indicated that performance of this method obtain significantly improved compared with other prediction method.
基于不同性质距离变换的dna结合蛋白预测模型
DNA结合蛋白是指一类可以与DNA结合产生复合物的蛋白质。它是DNA重组、修饰、复制、病毒整合和转录等细胞生命活动不可缺少的组成部分。随着基因测序技术的快速发展和对测序技术需求的不断增加,越来越多未知的dna结合蛋白等待着研究者去预测。然而,开发一种高质量、短时间的预测方法仍然面临着更多的挑战。本文提出了一种将位置特定频率矩阵(PSFM)和不同属性距离变换(DDT)相结合的新方法PSFM-DDT。首先用频率矩阵表达蛋白质序列的进化信息,然后利用不同氨基酸的距离变换将其转化为一系列新的特征向量。利用支持向量机(SVM)线性核方法对提取的向量特征进行训练,选择最后一个模型。该方法在基准数据集上进行叠刀测试,准确率达到83.16%,在独立数据集上达到79.57%。通过实验结果表明,与其他预测方法相比,该方法的性能得到了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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