Xiang Qilin, Liao Bo, Li Xianhong, Xu Huimin, Chen Jing, Shi Zhuoxing, Dai Qi, Yao Yuhua
School of Information Science and Engineering, Hunan University, Changsha 410082, China.
College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Artif Intell Med. 2017 May;78:41-46. doi: 10.1016/j.artmed.2017.05.007. Epub 2017 May 24.
In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins.
In the proposed methodology, the novel evolutionary-conservative information is introduced to represent protein sequences. Meanwhile, based on the proportion of golden section in mathematics, position-specific scoring matrix (PSSM) is divided into several blocks. Then, these features are predicted by support vector machine (SVM) and the predictive capability of proposed method is implemented by jackknife test RESULTS: The results show that the golden section method is better than no segmentation method. The overall accuracy for ZD98 and CL317 is 98.98% and 91.11%, respectively, which indicates that our method can play a complimentary role to the existing methods in the relevant areas.
The proposed feature representation is powerful and the prediction accuracy will be improved greatly, which denotes our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins.
本文提出一种用于预测凋亡蛋白亚细胞定位的高质量序列编码方案。
在所提出的方法中，引入了新颖的进化保守信息来表示蛋白质序列。同时，基于数学中的黄金分割比例，将位置特异性得分矩阵（PSSM）划分为若干块。然后，通过支持向量机（SVM）对这些特征进行预测，并通过留一法检验来实现所提方法的预测能力。结果：结果表明黄金分割法优于不进行分割的方法。ZD98和CL317的总体准确率分别为98.98%和91.11%，这表明我们的方法能够在相关领域对现有方法起到补充作用。
所提出的特征表示方法很强大，预测准确率将大幅提高，这意味着我们的方法在预测凋亡蛋白亚细胞定位方面提供了最先进的性能。
