计算机科学技术学报 ›› 2021,Vol. 36 ›› Issue (2): 276-287.doi: 10.1007/s11390-021-0740-2

所属专题： Emerging Areas

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基于逻辑加权剖面的双随机游走方法用于miRNA与疾病关联预测

Ling-Yun Dai, Member, CCF, Jin-Xing Liu*, Senior Member, CCF, Rong Zhu, Member, CCF, Juan Wang, Member, CCF, and Sha-Sha Yuan, Member, CCF   

  1. School of Computer Science, Qufu Normal University, Rizhao 276826, China
  • 收稿日期:2020-07-13 修回日期:2021-03-09 出版日期:2021-03-05 发布日期:2021-04-01
  • 通讯作者: Jin-Xing Liu E-mail:sdcavell@qfnu.edu.cn
  • 作者简介:Ling-Yun Dai received her B.S. degree in electronic information science and technology from the Physics Department, Shandong Normal University, Jinan, in 2001, and her M.S. degree in communication and information systems from School of Information Science and Engineering, Shandong University, Jinan, in 2004. Now she is an associate professor at School of Computer Science, Qufu Normal University, Rizhao. Her research interests include pattern recognition, machine learning, and bioinformatics.
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61902215, 61872220 and 61701279.

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

Ling-Yun Dai, Member, CCF, Jin-Xing Liu*, Senior Member, CCF, Rong Zhu, Member, CCF, Juan Wang, Member, CCF, and Sha-Sha Yuan, Member, CCF        

  1. School of Computer Science, Qufu Normal University, Rizhao 276826, China
  • Received:2020-07-13 Revised:2021-03-09 Online:2021-03-05 Published:2021-04-01
  • Contact: Jin-Xing Liu E-mail:sdcavell@qfnu.edu.cn
  • About author:Ling-Yun Dai received her B.S. degree in electronic information science and technology from the Physics Department, Shandong Normal University, Jinan, in 2001, and her M.S. degree in communication and information systems from School of Information Science and Engineering, Shandong University, Jinan, in 2004. Now she is an associate professor at School of Computer Science, Qufu Normal University, Rizhao. Her research interests include pattern recognition, machine learning, and bioinformatics.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61902215, 61872220 and 61701279.

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补充材料

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1、研究背景（context）
MicroRNAs（miRNAs）广泛存在于生物基因组中，由内源性基因编码。MiRNAs可调控细胞分化、增殖、凋亡、生长、衰老、病毒感染等相关基因的表达。越来越多的研究表明，具有相似序列和二级结构的miRNAs可以很容易地作用于相似的生物学过程，并且miRNAs的异常表达可引起多种人类疾病。传统的生物实验方法可以很准确地推断出miRNA-疾病关联。然而，这些方法通常是耗时和昂贵的。随着越来越多的生物学数据库出现，使用计算方法来预测未知的miRNA-疾病关联已经成为生物信息学的一个研究热点。现代生物医学研究需要高效的计算模型预测潜在的miRNA-疾病关联，了解疾病的机制，从而提高生物学实验的效率。
2、目的（Objective）：
我们的研究目的是设计一种高效的计算方法来预测miRNA-疾病关联，有助于人们理解人类疾病的发生和诊疗机制，促进药物研发。
3、方法（Method）：
本文提出了LWBRW方法来预测潜在的miRNA-疾病关联。该方法首先，利用高斯互作谱和逻辑函数构建miRNA相似性网络和疾病相似性网络，并通过已知的miRNA-疾病关联将miRNA相似性网络和疾病相似性网络联系起来；其次，本文利用weighted K-nearest known neighbours（WKNKN）方法对已知的关联信息进行预处理；随后，在miRNA相似性网络和疾病相似性网络上使用双随机游走预测潜在的miRNA-疾病关联；最后，使用五折交叉验证和留一法评估LWBRW方法的预测性能，并通过案例研究证明该方法能够有效地预测潜在的miRNA-疾病关联。
4、结果（Result&Findings）：
本文将LWBRW方法的关联预测能力与WBSMDA，LRSSLMDA，PBMDA，HDMP和RLSMDA五种方法相比较。五折交叉验证运行10次取平均值，LWBRW方法的AUC值为0.9393（0.0061），超过了其他五种方法：WBSMDA（0.8185），LRSSLMDA（0.9181），PBMDA（0.9172），HDMP（0.8432），RLSMDA（0.8569）。使用留一法评估，LWBRW方法的AUC值达到0.9763，而其他五种方法的AUC值分别为WBSMDA（0.8031），LRSSLMDA（0.9178），PBMDA（0.9169），HDMP（0.9431），RLSMDA（0.9511）。本文还进一步对前列腺肿瘤、肝脏肿瘤和淋巴瘤进行案例分析，证明LWBRW方法辨识miRNA-疾病关联的能力。
5、结论（Conclusions）：
我们通过交叉验证和案例分析证明了该方法能够有效地辨识潜在的miRNA-疾病关联。然而，LWBRW方法也有一些不足之处。例如，一些重要的生物学信息，比如miRNAs的功能信息和疾病的表型信息，可以提高LWBRW算法的准确性和可靠性；已知的miRNA-疾病关联矩阵是高度稀疏的，积累和收集更多的实验证据，可以进一步验证该方法的预测能力。

关键词: miRNA-疾病关联, 逻辑函数, 高斯互作谱, 加权已知k近邻, 双随机游走

Abstract: MicroRNAs (miRNAs) exert an enormous influence on cell differentiation, biological development and the onset of diseases. Because predicting potential miRNA-disease associations (MDAs) by biological experiments usually requires considerable time and money, a growing number of researchers are working on developing computational methods to predict MDAs. High accuracy is critical for prediction. To date, many algorithms have been proposed to infer novel MDAs. However, they may still have some drawbacks. In this paper, a logistic weighted profile-based bi-random walk method (LWBRW) is designed to infer potential MDAs based on known MDAs. In this method, three networks (i.e., a miRNA functional similarity network, a disease semantic similarity network and a known MDA network) are constructed first. In the process of building the miRNA network and the disease network, Gaussian interaction profile (GIP) kernel is computed to increase the kernel similarities, and the logistic function is used to extract valuable information and protect known MDAs. Next, the known MDA matrix is preprocessed by the weighted K-nearest known neighbours (WKNKN) method to reduce the number of false negatives. Then, the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network. Finally, the predictive ability of the LWBRW method is confirmed by the average AUC of 0.939 3 (0.006 1) in 5-fold cross-validation (CV) and the AUC value of 0.976 3 in leave-one-out cross-validation (LOOCV). In addition, case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

Key words: miRNA-disease association, logistic function, Gaussian interaction profile, weighted K-nearest known neighbour, bi-random walk

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