MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain the original topology and biological functions of subnetworks and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.

Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.

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.

The tremendous progress of single-cell sequencing technology has given researchers the opportunity to study cell development and differentiation processes at single-cell resolution. Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) was proposed for genome-wide analysis of chromatin accessibility. Due to technical limitations or other reasons, dropout events are almost a common occurrence for extremely sparse single-cell ATAC-seq data, leading to confusion in downstream analysis (such as clustering). Although considerable progress has been made in the estimation of scRNA-seq data, there is currently no specific method for the inference of dropout events in single-cell ATAC-seq data. In this paper, we select several state-of-the-art scRNA-seq imputation methods (including MAGIC, SAVER, scImpute, deepImpute, PRIME, bayNorm and knn-smoothing) in recent years to infer dropout peaks in scATAC-seq data, and perform a systematic evaluation of these methods through several downstream analyses. Specifically, we benchmarked these methods in terms of correlation with meta-cell, clustering, subpopulations distance analysis, imputation performance for corruption datasets, identification of TF motifs and computation time. The experimental results indicated that most of the imputed peaks increased the correlation with the reference meta-cell, while the performance of different methods on different datasets varied greatly in different downstream analyses, thus should be used with caution. In general, MAGIC performed better than the other methods most consistently across all assessments. Our source code is freely available at https://github.com/yueyueliu/scATAC-master.

The association between microRNAs ( miRNAs) and diseases is significant to understand the development and progression of many human diseases. Given the cost and complexity of biological experiments, the computational method for predicting the potential association between miRNAs and disease will be an effective complement. In this article, we have developed a model (microRNA and disease based on Bayesian probabilistic matrix factorization, MDBPMF) based on a fully Bayesian treatment of the probabilistic matrix factorization to find potential associations between miRNAs and diseases by using the HMDDv2.0 database, which contains proven miRNA–disease associations. We show that Bayesian probabilistic matrix factorization models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the HMDDv2.0 database. MDBPMF achieves reliable prediction with an average area under receiver operating characteristic curve of 0.8755 for eight complex diseases based on fivefold cross-validation, which indeed outperforms the state-of-the-art method. In addition, a case study of lung cancer further verifies the utility of our method.