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基于独立性标准的多视图局部超平面近邻模型,用于识别囊泡运输蛋白

Rui Fan¹, Yijie Ding², Quan Zou¹

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang 324000, China.

International journal of biological macromolecules

|July 12, 2023

概括

我们开发了一种新型分类器,HSIC-GHKNN,以准确识别囊泡运输蛋白,这对于细胞功能至关重要,并且与人类疾病有关. 这种方法显示了比现有技术更好的性能.

科学领域:

生物化学生物化学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

膀运输蛋白对于细胞内物质的运动至关重要,并与各种人类疾病有关.
准确识别这些蛋白质对于了解疾病机制和制定治疗策略至关重要.

研究的目的:

开发一种新的,准确的计算策略来识别囊泡运输蛋白.
为此识别任务引入图形规律化的k-局部超平面距离近邻 (HSIC-GHKNN) 模型.

主要方法:

使用伪位置特定得分矩阵 (PsePSSM) 和AATP来提取蛋白质进化信息.
应用了编辑的最近邻居 (ENN) 算法来处理数据集不平衡.
开发了一个多视图分类器 (HSIC-GHKNN) 结合希尔伯特-施密特独立性标准 (HSIC) 和局部超平面距离最近邻居.

主要成果:

在一个独立的测试组中,获得了85.8%的准确性和0.548的马修相关系数.
在大多数评估指标上,HSIC-GHKNN模型与现有方法相比表现优越.
分析确定了PsePSSM和AATP特征集中的关键影响因素.

结论:

拟议的HSIC-GHKNN战略提供了一种强大而准确的方法来识别囊泡运输蛋白.
多视图分类模型可以适应类似的蛋白质识别挑战.
这项工作有助于更好地了解囊泡运输蛋白及其在健康和疾病中的作用.

关键词:

独立性标准的独立性标准当地超平面局部超平面多视图学习多视图学习蛋白质功能的预测和预测囊泡运输蛋白质囊泡运输蛋白质

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