Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations
Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations
复制标题
具有关系约束的张量分解，用于预测多种类型的 microRNA 疾病关联。
DOI：
10.1093/bib/bbaa140
复制
发表时间：
2021-05-01
影响因子：
9.5
通讯作者：
Zhang, Wen
中科院分区：
文献类型：
--
作者：
Huang, Feng;Yue, Xiang;Zhang, Wen
microRNAs（miRNAs）在与人类疾病相关的多种生物学过程中起着至关重要的作用。识别潜在的miRNA-疾病关联有助于理解miRNA相关疾病的分子机制。现有的大多数计算方法主要集中在预测是否存在miRNA与疾病的关联。然而，miRNAs在疾病中的作用存在显著差异，例如，miRNAs的遗传变异体（mir-15）可能影响miRNAs的表达水平，导致B细胞慢性淋巴细胞白血病，而循环miRNAs（包括mir-1246、mir-1307- 3 p等）可能影响miRNAs的表达水平。具有早期发现乳腺癌的潜力。在本文中，我们的目标是预测多种类型的miRNA与疾病的关联，而不是将它们视为二元的。为此，我们创新性地将miRNA疾病类型三元组表示为张量，并引入张量分解方法来解决预测任务。在两个广泛采用的miRNA疾病数据集：HMDD v2.0和HMDD v3.2上的实验结果表明，张量分解方法在很大程度上改善了最近的基线（在Top-1F 1中高达38%）。然后，我们提出了一种新的方法，张量分解与关系约束（TDRC），它结合了生物特征的关系约束，进一步现有的张量分解方法。与现有的两种张量分解方法相比，TDRC具有更好的性能和更高的效率。
MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of miRNA (mir-15) may affect the expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we aim to predict multi-type miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triples as a tensor and introduce tensor decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to 38% in Top-1F1). We then propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient.