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Research Article

基于布尔矩阵的层次扩展用于LncRNA疾病关联预测

卷 20, 期 6, 2020

页: [452 - 460] 页: 9

弟呕挨: 10.2174/1566524019666191119104212

价格: $65

摘要

背景：大量的实验研究表明，长的非编码RNA（LncRNA）在各种复杂的人类疾病的发生和发展过程中起着至关重要的作用。但是，目前只有一小部分LncRNA与疾病的关联已通过实验验证。基于计算模型自动预测LncRNA与疾病的关联可以节省湿实验室实验的巨额成本。 方法和结果：为了建立有效的计算模型以整合各种异质生物学数据以鉴定潜在疾病-LncRNA，我们提出了基于布尔矩阵的LncRNA-疾病关联预测模型（HEBLDA）的层次扩展。 HEBLDA根据来自各种关系源的布尔矩阵的属性发现内在的层次相关性。然后，HEBLDA通过融合权重将这些层次关联的矩阵进行集成。最后，HEBLDA使用分层关联矩阵通过分层扩展来重建LncRNA-疾病关联矩阵。没有已知的关联数据，HEBLDA能够治疗潜在的疾病或LncRNA。在5倍交叉验证实验中，HEBLDA在接收器工作特性曲线（AUC）下获得了0.8913的面积，从而改进了以前的经典方法。此外，案例研究表明，HEBLDA可以准确预测几种LncRNA的候选疾病。 结论：基于其发现各种数据源更丰富的关联结构的能力，我们可以预期HEBLDA是一种潜在的方法，可以在广阔的领域获得更全面的关联预测。

关键词: LncRNA，疾病，关联预测，布尔矩阵，分层扩展，关联矩阵。

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