关联知识图用于有效的序列存储和检索
Przemysław Stokłosa1, Janusz A Starzyk2, Paweł Raif3
1Institute of Management and Information Technology, Bielsko-Biała, Poland.
Computer methods and programs in biomedicine
|June 6, 2025
概括
本研究介绍了顺序结构关联知识图 (SSAKGs) 进行有效的序列存储和检索. 权重边缘节点排序算法在异常检测和遗传分析等任务中实现了高精度.
科学领域:
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
- 计算神经科学是一种神经科学.
背景情况:
- 序列存储和检索在异常检测,行为预测和遗传分析方面存在挑战.
- 关联知识图 (AKG) 通过使用稀疏图结构编码序列来提供解决方案.
- 现有的方法需要提高内存容量和基于上下文的检索精度.
研究的目的:
- 开发一种使用关联知识图 (AKG) 进行序列存储和检索的有效方法.
- 引入用于优化AKG内部元素排序的算法.
- 为了保持高内存容量和基于上下文的检索精度.
主要方法:
- 利用了序列结构关联知识图 (SSAKGs),将序列编码为过渡性比赛.
- 开发并测试了四种排序算法:简单排序,节点排序,增强节点排序和加权边缘节点排序.
- 在合成和现实数据集 (句子,miRNA序列) 上使用精度,灵敏度和特异性评估性能.
主要成果:
- 权重边缘节点排序算法显示出卓越的精度和图形密度弹性.
- 在句子检索 (94.7%-97.3%) 和miRNA序列检索 (99.6%) 中实现了高精度.
- 在SSAKG中,相对于图形大小,记忆容量呈现出二次增长.
结论:
- 引入了一种用于序列存储和检索的新型结构方法,没有培训要求.
- 突出灵活的基于上下文的重建和稀疏内存图的高效率.
- 该方法为计算神经科学和生物信息学中的基于序列的记忆任务提供了可扩展的解决方案.
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