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Review Article

对计算模型ubiquitination网站识别蛋白

卷 20, 期 5, 2019

页: [565 - 578] 页: 14

弟呕挨: 10.2174/1389450119666180924150202

价格: $65

摘要

泛素化是一个重要的翻译后修饰（PTM）过程，用于调节蛋白质功能，这与癌症、心血管和其他疾病有关。最近的举措集中在借助物理化学测试方法以及计算方法的应用来检测潜在的泛素化位点。利用实验室试验确定泛素化位点尤其容易受到泛素化过程的时间性和可逆性的影响，而且成本高且耗时长。研究表明，计算方法可以有效地从生物序列集合中提取潜在的规则或推论。到目前为止，计算策略一直是应用于泛素化位点识别的关键研究方法之一，目前，有许多从机器学习和统计分析发展而来的最先进的计算方法来承担这项工作。本研究综述了基准数据集的构建，以及特征表示方法、特征选择方法和以往出版物中涉及的分类器。为了探索泛素化位点识别的相关发展趋势，本文构建了一个独立的测试数据集，并报告了5种预测工具的预测结果，以及一些相关的讨论。

关键词: 蛋白质泛素化，计算方法，数据采集，特征提取，特征选择，预测模型。

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