Inferring Latent Disease-lncRNA Associations by Faster Matrix Completion on a Heterogeneous Network

Abstract

Current studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in a variety of fundamental biological processes related to complex human diseases. The prediction of latent disease-lncRNA associations can help to understand the pathogenesis of complex human diseases at the level of lncRNA, which also contributes to the detection of disease biomarkers, and the diagnosis, treatment, prognosis and prevention of disease. Nevertheless, it is still a challenging and urgent task to accurately identify latent disease-lncRNA association. Discovering latent links on the basis of biological experiments is time-consuming and wasteful, necessitating the development of computational prediction models. In this study, a computational prediction model has been remodeled as a matrix completion framework of the recommendation system by completing the unknown items in the rating matrix. A novel method named faster randomized matrix completion for latent disease-lncRNA association prediction (FRMCLDA) has been proposed by virtue of improved randomized partial SVD (rSVD-BKI) on a heterogeneous bilayer network. First, the correlated data source and experimentally validated information of diseases and lncRNAs are integrated to construct a heterogeneous bilayer network. Next, the integrated heterogeneous bilayer network can be formalized as a comprehensive adjacency matrix which includes lncRNA similarity matrix, disease similarity matrix, and disease-lncRNA association matrix where the uncertain disease-lncRNA associations are referred to as blank items. Then, a matrix approximate to the original adjacency matrix has been designed with predicted scores to retrieve the blank items. The construction of the approximate matrix could be equivalently resolved by the nuclear norm minimization. Finally, a faster singular value thresholding algorithm with a randomized partial SVD combing a new sub-space reuse technique has been utilized to complete the adjacency matrix. The results of leave-one-out cross-validation (LOOCV) experiments and 5-fold cross-validation (5-fold CV) experiments on three different benchmark databases have confirmed the availability and adaptability of FRMCLDA in inferring latent relationships of disease-lncRNA pairs, and in inferring lncRNAs correlated with novel diseases without any prior interaction information. Additionally, case studies have shown that FRMCLDA is able to effectively predict latent lncRNAs correlated with three widespread malignancies: prostate cancer, colon cancer, and gastric cancer.

An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2

“…In our study, weighted soft voting is utilized to ensemble multiple matrix completion models to predict potential associated drugs against the virus SARS-CoV-2. We fitted the known virus-drug associations, virus integrated similarities, and drug integrated similarities into seven matrix completion models, including the model of Graph Regularized Matrix Factorization (GRMF) ( Ezzat et al, 2017 ), Inductive Matrix Completion (IMC) ( Chen et al, 2018b ), Bounded Nuclear Norm Regularization (BNNR) ( Yang et al, 2019 ), Faster Randomized Matrix Completion (FRMC) ( Li et al, 2019 ), Deep Matrix Factorization (DMF) ( Fan and Cheng, 2018 ), Deep Learning-based Matrix Completion (DLMC) ( Fan and Chow, 2017 ), and Heterogeneous Graph Inference with Matrix Completion (HGIMC) ( Yang et al, 2021 ). All of these models have performed well in the field of biological association prediction.…”

“…Fast Randomized Matrix Completion (FRMC) is a computational model that has been used to predict the latent associations between lncRNAs and diseases ( Li et al, 2019 ). It is a type of rank minimization model.…”

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

“…The three methods above are based on matrix factorization, and the difference is that the latter two add weight and probability separately. [43][44][45] Li et al 46 developed a computational method, DNILMF-LDA, that is anchored in the neighborhood regularized logistic matrix factorization and optimizes the above parameters to predict interaction probabilities. NNLDA, determined by Hu et al, 47 solved some of the disadvantages of traditional matrix factorization by changing the training method and the loss function and adding a fully connected layer.…”

“…Li et al 46 developed a computational model of faster randomized matrix completion for latent disease-lncRNA association (named FRMCLDA) that used the fSVT algorithm to predict lncRNA-disease associations based on the idea of matrix completion. FRMCLDA uses the disease similarity matrix, lncRNA similarity matrix, lncRNA-disease association matrix, and transpose matrix of the association matrix to construct the adjacency matrix, which improves the prediction performance by fitting the adjacency matrix.…”

A Novel lncRNA-Disease Association Prediction Model Using Laplacian Regularized Least Squares and Space Projection-Federated Method

“…Xuan et al proposed a variety of methods on the basis of convolutional neural network for the prediction of disease-related lncRNAs [45][46][47]. In recent years, other machine learning-based models, such as randomized matrix completion algorithm [48], inductive matrix completion algorithm [49], and matrix factorization algorithm [50], have been put forward to predict lncRNA-associated diseases.Liu et al [51]proposed a weighted graph regulated collaborative matrix factorization method to identify lncRNA-disease associations; Xuan et al [52] used the probabilistic matrix factorization method to predict disease-related lncRNAs; Zeng et al [53] integrated alternative lead squares and matrix factorization to identify lncRNA-disease associations; Zeng et al [54]integrated deep learning and matrix factorization to identify lncRNA-disease association.However, parameters are difficult to determine via such methods.Xuan et al [52] used the probabilistic matrix factorization method to predict disease-related lncRNAs.…”