Clinical trials indicate that the dysregulation of microRNAs (miRNAs) is closely associated with the development of diseases. Therefore, predicting miRNA-disease associations is significant for studying the pathogenesis of diseases. Since traditional wet-lab methods are resource-intensive, cost-saving computational models can be an effective complementary tool in biological experiments. In this work, a locality-constrained linear coding is proposed to predict associations (ILLCEL). Among them, ILLCEL adopts miRNA sequence similarity, miRNA functional similarity, disease semantic similarity, and interaction profile similarity obtained by locality-constrained linear coding (LLC) as the priori information. Next, features and similarities extracted from multiperspectives are input to the ensemble learning framework to improve the comprehensiveness of the prediction. Significantly, the introduction of hypergraph-regular terms improves the accuracy of prediction by describing complex associations between samples. The results under fivefold cross validation indicate that ILLCEL achieves superior prediction performance. In case studies, known associations are accurately predicted and novel associations are verified in HMDD v3.2, miRCancer, and existing literature. It is concluded that ILLCEL can be served as a powerful tool for inferring potential associations.

中文翻译：

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通过基于局部约束线性编码的集成学习识别疾病相关的 MicroRNA。

临床试验表明，microRNA（miRNA）的失调与疾病的发生密切相关。因此，预测miRNA与疾病的关联对于研究疾病的发病机制具有重要意义。由于传统的湿实验室方法是资源密集型的，因此节省成本的计算模型可以成为生物实验中的有效补充工具。在这项工作中，提出了一种局部约束线性编码来预测关联（ILLCEL）。其中，ILLCEL采用局部约束线性编码（LLC）获得的miRNA序列相似性、miRNA功能相似性、疾病语义相似性和相互作用谱相似性作为先验信息。接下来，从多视角提取的特征和相似性被输入到集成学习框架中，以提高预测的全面性。值得注意的是，超图正则项的引入通过描述样本之间的复杂关联提高了预测的准确性。五重交叉验证的结果表明ILLCEL实现了优异的预测性能。在案例研究中，已知的关联被准确预测，新的关联在 HMDD v3.2、miRCancer 和现有文献中得到验证。结论是 ILLCEL 可以作为推断潜在关联的强大工具。