| 引用本文: | 闫婷,李凤敏.基于支持向量机识别亚高尔基体蛋白质的定位[J].生物信息学,2023,21(1):45-50. |
| YAN Ting,LI Fengmin.Identification of sub-Golgi proteins localization based on support vector machine[J].Chinese Journal of Bioinformatics,2023,21(1):45-50. |
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| 摘要: |
| 研究表明,许多神经退行性疾病都与蛋白质在高尔基体中的定位有关,因此,正确识别亚高尔基体蛋白质对相关疾病药物的研制有一定帮助,本文建立了两类亚高尔基体蛋白质数据集,提取了氨基酸组分信息、联合三联体信息、平均化学位移、基因本体注释信息等特征信息,利用支持向量机算法进行预测,基于5-折交叉检验下总体预测成功率为87.43%。 |
| 关键词: 亚高尔基体 蛋白质 氨基酸组分 基因本体 支持向量机 |
| DOI：10.12113/202202013 |
| 分类号:Q61 |
| 文献标识码:A |
| 基金项目:内蒙古自治区自然科学基金项目(No.2019MS03015). |
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| Identification of sub-Golgi proteins localization based on support vector machine |
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YAN Ting,LI Fengmin
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(College of Science, Inner Mongolia Agricultural University, Hohhot 010018, China)
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| Abstract: |
| Many neurodegenerative diseases are associated with the location of proteins in the Golgi apparatus. Therefore, the correct identification of sub-Golgi proteins is helpful for the development of drugs for related diseases. In this study, two types of sub-Golgi protein datasets were established. On the basis of the amino acid composition information, the conjoint triad feature information, the auto-covariance average chemical shift, and the gene ontology information, the localization of sub-Golgi protein was predicted by using the algorithm of support vector machine. The overall prediction accuracy was 87.43% in the 5-fold cross-validation. |
| Key words: sub-Golgi Protein Amino acid composition Gene ontology Support vector machine |
