| 引用本文: | 高光芹,黄家荣,周俊朝,谢鹏芳.杨树蛋白质磷酸化位点预测[J].生物信息学,2015,13(3):165-169. |
| GAO Guangqin,HUANG Jiarong,ZHOU Junchao,XIE Pengfang.Predicting phosphorylation sites of Poplar protein[J].Chinese Journal of Bioinformatics,2015,13(3):165-169. |
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| 摘要: |
| 以小黑杨磷酸化蛋白质组为研究对象,用人工神经网络表达丝氨酸、苏氨酸等残基位点的磷酸化与氨基酸序列的结构特征之间的非线性关系,建立了BP人工神经网络模型,并用磷酸化数据对所建模型进行训练和分析,得适宜的结构为21×16∶8∶4,拟合准确度为90%,Acc、Sn、Sp、MCC分别为78%、89%、67%、0.57,对比分析结果表明,所建模型具有较强的预测能力。 |
| 关键词: 小黑杨 磷酸化蛋白质 磷酸化位点 人工神经网络 |
| DOI：10.3969/j.issn.1672-5565.2015.03.04 |
| 分类号:Q51 |
| 基金项目:河南省高等学校重点科研项目。 |
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| Predicting phosphorylation sites of Poplar protein |
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GAO Guangqin, HUANG Jiarong, ZHOU Junchao, XIE Pengfang
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(Henan Agricultural University, Zhengzhou 450002, China)
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| Abstract: |
| In this paper, the phosphoproteome of Populus simonii×P nigra was used as the research object. The nonlinear relationship between the structure characteristics of amino acid sequence and phosphorylation of serine and threonine was expressed by artificial neural network. A BP artificial neural network model was established and trained by using the real data on phosphorylation. The appropriate structure is 21 x 16∶8∶4, the fitting accuracy is 90%, and the Acc, Sn, Sp, MCC are 78%, 89%, 67%, and 0.57, respectively. The comparative results show that the model has strong prediction ability. |
| Key words: Populus simonii×P nigra Phosphoproteome Phosphorylation site Artificial neural network |
