Predicting miRNA–disease association based on inductive matrix completion

et al. 2020

BMC Bioinformatics

Background: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers.Results: Here, we present a computational framework based on graph Laplacian regularized L 2, 1 -nonnegative matrix factorization (GRL 2, 1 -NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL 2,1 -NMF framework was used to predict links between microRNAs and diseases. Conclusions:The new method (GRL 2, 1 -NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL 2, 1 -NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.

Section: Performance Evaluationmentioning

confidence: 99%

Section: Gaussian Interaction Profile Kernel Similarity For Diseases mentioning

confidence: 99%

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction

Gao

et al. 2020

BMC Bioinformatics

“…In 2018, Chen et al put forward a novel calculation method of Ensemble Learning and Link Prediction for miRNA‐Disease Association prediction (ELLPMDA), in which they gained final scores for the novel miRNA‐disease associations through weighted combining the three outcomes obtained from common neighbours, Jaccard index and Katz index, respectively. In the same year, Chen et al further introduced a model of Inductive Matrix Completion for MiRNA‐Disease Association prediction (IMCMDA) through implementing the low‐rank inductive matrix completion method on the basis of the data set of known miRNA‐disease associations, miRNA similarity and disease similarity.…”

Section: Introductionmentioning

confidence: 99%

“…There are many computational methods proposed to predict the potential associations between miRNAs and diseases, most of which are developed based on the assumption that miRNAs with similar functions are more likely to have connections with diseases of similar phenotypes. [16][17][18][19][20][21] Every time a new model was proposed, the prediction accuracy would be increased. In 2010, a hypergeometric distribution-based model was presented by Jiang et al 22 to predict miRNA-disease associations, where disease phenotype similarity, miRNA functional similarity and known human disease-miRNA associations were integrated.…”

Section: Introductionmentioning

confidence: 99%

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

Guan

J Cellular Molecular Medi

Zhang

et al. 2019

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion‐based Regularized Least Squares for MiRNA‐Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA‐disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave‐one‐out cross‐validation (LOOCV) and 5‐fold cross‐validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5‐fold cross‐validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

“…DNRLMF-MDA was proposed to discover hidden miRNA-disease associations based on known miRNA-disease associations, miRNA similarity and disease similarity, the main feature of DNRLMF-MDA was that it assigned higher importance levels to the observed interacting miRNA-disease pairs than unknown pairs [21]. Based on the inductive matrix completion model, IMCMDA was also proposed to predict miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity [22]. Chen et al [23] proposed a computational model named Laplacian regularized sparse subspace learning for miRNA-disease association prediction (LRSSLMDA), which projected miRNA/disease' statistical feature profiles and graph theoretical feature profiles to a common subspace.…”

mentioning

confidence: 99%

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences

Cheng

et al. 2020

BMC Bioinformatics

Background: MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result: In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion: The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.