ArticleJournal of cellular and molecular medicine2020
In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.
Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- NutraceuticalCurrent pharmaceutical design · 2026Article
- Decoding Ocimum basilicum L.'s Medicinal Potential Through Growth Phase Profiling: A Conservation Perspective.Chemistry & biodiversity · 2026Article
- Article
- Mechanism of Phellodendron and Anemarrhena Drug Pair on the Treatment of Liver Cancer Based on Network Pharmacology and Bioinformatics.Frontiers in oncology · 2022Article
- RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation.Molecular genetics and genomics : MGG · 2021Review
- Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data.Scientific reports · 2020Article
- In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion.Journal of cellular and molecular medicine · 2020Article
- QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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