ArticleBMC genomics2018
FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest.Interdisciplinary sciences, computational life sciences · 2022Article
- RFLMDA: A Novel Reinforcement Learning-Based Computational Model for Human MicroRNA-Disease Association Prediction.Biomolecules · 2021Article
- Identifying potential association on gene-disease network via dual hypergraph regularized least squares.BMC genomics · 2021Article
- A sequence-based multiple kernel model for identifying DNA-binding proteins.BMC bioinformatics · 2021Article
- RNA-Associated Co-expression Network Identifies Novel Biomarkers for Digestive System Cancer.Frontiers in genetics · 2021Article
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- 4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.Frontiers in cell and developmental biology · 2021Article
- iDNA-MT: Identification DNA Modification Sites in Multiple Species by Using Multi-Task Learning Based a Neural Network Tool.Frontiers in genetics · 2021Article
- Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.Frontiers in genetics · 2021Article
- Kernel Fusion Method for Detecting Cancer Subtypes via Selecting Relevant Expression Data.Frontiers in genetics · 2020Article
- A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.Frontiers in bioengineering and biotechnology · 2020Article
- Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification.BioMed research international · 2020Article
- A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment.BioMed research international · 2020Article
- PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.Computational and mathematical methods in medicine · 2020Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- Selecting Essential MicroRNAs Using a Novel Voting Method.Molecular therapy. Nucleic acids · 2019Article
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- Molecular Computing and Bioinformatics.Molecules (Basel, Switzerland) · 2019Article
- anamiR: integrated analysis of MicroRNA and gene expression profiling.BMC bioinformatics · 2019Article
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations.
resultFirst, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs.
conclusionsOur proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.
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