Li Beier, Zhong Kaiyang, Deveci Muhammet, Tang Yong
School of Statistics, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872, China.
College of Information Science & Electronic Engineering, Zhejiang University, No. 866 Yuhangtang Road, Xihu District, Hangzhou 310058, Zhejiang Province, China.
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf444.
Exploring latent microRNA (miRNA)-disease associations (MDAs) is vital for early screening and treatment. Compared with traditional experiments, computational methods enhance efficiency and lower costs in predicting MDAs. We trained the Accurate Matrix Completion for predicting potential MiRNA-Disease Associations (AMCMDA) model in this work, utilizing truncated nuclear norm minimization to improve the prediction accuracy. In AMCMDA, we begin by constructing a heterogeneous network incorporating both similarity and association information between miRNAs and diseases. Second, an optimization framework is designed to complete the effective approximation of the truncated nuclear norm to complement the missing values of the objective matrix. Finally, we solve this optimization problem via Alternating Direction Method of Multipliers and obtain the final prediction scores. After comparing the AMCMDA model with other models across three validation frameworks and three different datasets, we find that the AMCMDA model demonstrates robust and accurate performance. The model's excellent performance is also demonstrated by two categories of case studies on three diseases.
探索潜在的微小RNA（miRNA）与疾病的关联（MDA）对于早期筛查和治疗至关重要。与传统实验相比，计算方法在预测MDA方面提高了效率并降低了成本。在这项工作中，我们训练了用于预测潜在miRNA-疾病关联的精确矩阵补全（AMCMDA）模型，利用截断核范数最小化来提高预测准确性。在AMCMDA中，我们首先构建一个包含miRNA和疾病之间相似性和关联信息的异质网络。其次，设计一个优化框架来完成截断核范数的有效近似，以补充目标矩阵的缺失值。最后，我们通过交替方向乘子法解决这个优化问题，并获得最终的预测分数。在三个验证框架和三个不同数据集上，将AMCMDA模型与其他模型进行比较后，我们发现AMCMDA模型表现出稳健且准确的性能。针对三种疾病的两类案例研究也证明了该模型的优异性能。
