Hessian Regularized $$L_{2,1}$$ -Nonnegative Matrix Factorization and Deep Learning for miRNA–Disease Associations Prediction

IF 3.9 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2023-12-15 DOI:10.1007/s12539-023-00594-8

Guo-Sheng Han, Qi Gao, Ling-Zhi Peng, Jing Tang

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Abstract

Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA–disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA–disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized $L_{2,1}$ nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as $HRL_{2,1}$-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA–disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.

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Hessian Regularized $$L_{2,1}$$ -Nonnegative Matrix Factorization（黑森正则化$$L_{2,1}$$）和深度学习用于 miRNA 与疾病关联预测

摘要自从发现微小核糖核酸（miRNA）以来，实证研究已经证明了它们在生物体功能中的重要作用。对 miRNA 的研究极大地促进了与避免、诊断和治疗人类复杂疾病有关的工作。然而，在传统的湿法实验中，探索每一种可以想象的 miRNA 与疾病的关联都要耗费大量的资源和时间。在计算方面，预测潜在的 miRNA 与疾病的联系可为医学研究人员提供宝贵的初步见解。因此，我们开发了一种新颖的矩阵因式分解模型，称为黑森规则化（Hessian-regularized $L_{2,1}$）非负矩阵因式分解，并将其与深度学习相结合，用于预测 miRNA 与疾病之间的关联，称为 $HRL_{2,1}$-NMF-DF。我们特别引入了一种新颖的迭代融合方法来整合所有相似性。这种方法有效地减少了初始 miRNA-疾病关联矩阵的稀疏性。此外，我们还设计了一个混合模型框架，利用深度学习、矩阵分解和奇异值分解来捕捉和描述 miRNA 与疾病之间错综复杂的非线性特征。通过对比分析、相似性矩阵融合、数据预处理和参数调整，六种矩阵因式分解方法的预测性能得到了提高。在五倍交叉验证下，新矩阵因式分解模型得到的 AUC 和 AUPR 与其他矩阵因式分解模型相当或更好。最后，我们选取肺肿瘤、膀胱肿瘤和乳腺肿瘤三种疾病进行病例分析，并进一步扩展了基于深度学习的矩阵因式分解模型。结果表明，本文提出的矩阵因式分解与深度学习相结合的混合算法可以高精度预测与不同疾病相关的 miRNA。图文摘要

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来源期刊

Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

8.60

自引率

4.20%

发文量

期刊介绍： Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.