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.

中文翻译：

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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。

MicroRNAs (miRNAs) 在与人类疾病相关的多种生物过程中起着至关重要的作用。识别潜在的 miRNA 与疾病的关联有助于理解 miRNA 相关疾病的分子机制。大多数现有的计算方法主要集中在预测是否存在 miRNA-疾病关联。然而，miRNA在疾病中的作用存在显着差异，例如，miRNA（mir-15）的遗传变异可能会影响导致 B 细胞慢性淋巴细胞白血病的 miRNA 的表达水平，而循环 miRNA（包括 mir-1246、mir-1307-3p 等）具有检测乳腺癌的潜力。早期。在本文中，我们的目标是预测多类型 miRNA 与疾病的关联，而不是将它们视为二元的。为此，我们创新地将 miRNA 疾病类型的三元组表示为张量，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，张量分解方法大规模改善了最近的基线（高达|$38\%$|在 Top-1F1）。然后，我们提出了一种新方法，即具有关系约束的张量分解（TDRC），它结合了生物特征作为关系约束来进一步改进现有的张量分解方法。与现有的两种张量分解方法相比，TDRC 可以在更高效的同时产生更好的性能。