Wang Lin, Chen Yaguang, Zhang Naiqian, Chen Wei, Zhang Yusen, Gao Rui
School of Mathematics and Statistics, Shandong University, Jinan, China.
School of Control Science and Engineering, Shandong University, Jinan, China.
Front Genet. 2020 Oct 22;11:594796. doi: 10.3389/fgene.2020.594796. eCollection 2020.
Studies have shown that microRNAs (miRNAs) are closely associated with many human diseases, but we have not yet fully understand the role and potential molecular mechanisms of miRNAs in the process of disease development. However, ordinary biological experiments often require higher costs, and computational methods can be used to quickly and effectively predict the potential miRNA-disease association effect at a lower cost, and can be used as a useful reference for experimental methods. For miRNA-disease association prediction, we have proposed a new method called Matrix completion algorithm based on q-kernel information (QIMCMDA). We use fivefold cross-validation and leave-one-out cross-validation to prove the effectiveness of QIMCMDA. LOOCV shows that AUC can reach 0.9235, and its performance is significantly better than other commonly used technologies. In addition, we applied QIMCMDA to case studies of three human diseases, and the results show that our method performs well in inferring potential interaction between miRNAs and diseases. It is expected that QIMCMDA will become an excellent supplement in the field of biomedical research in the future.
研究表明，微小RNA（miRNA）与许多人类疾病密切相关，但我们尚未完全了解miRNA在疾病发展过程中的作用及潜在分子机制。然而，普通生物学实验往往成本较高，而计算方法可以以较低成本快速有效地预测潜在的miRNA-疾病关联效应，并可作为实验方法的有用参考。对于miRNA-疾病关联预测，我们提出了一种基于q核信息的矩阵补全算法（QIMCMDA）的新方法。我们使用五折交叉验证和留一法交叉验证来证明QIMCMDA的有效性。留一法交叉验证表明，AUC可达0.9235，其性能明显优于其他常用技术。此外，我们将QIMCMDA应用于三种人类疾病的案例研究，结果表明我们的方法在推断miRNA与疾病之间的潜在相互作用方面表现良好。预计QIMCMDA未来将成为生物医学研究领域的优秀补充。
