ArticleNucleic acids research2013
Heterogeneous ensemble approach with discriminative features and modified-SMOTEbagging for pre-miRNA classification.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 70 citations in OpenAlex.
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- Species-specific microRNA discovery and target prediction in the soybean cyst nematode.Scientific reports · 2023Article
- mSRFR: a machine learning model using microalgal signature features for ncRNA classification.BioData mining · 2022Article
- Integrated Full-Length Transcriptome and MicroRNA Sequencing Approaches Provide Insights Into Salt Tolerance in Mangrove (Frontiers in genetics · 2022Article
- [Predicting prolonged length of intensive care unit stayBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2021Article
- Predicting Prolonged Length of ICU Stay through Machine Learning.Diagnostics (Basel, Switzerland) · 2021Article
- Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis-Treated Patients Using Stacked Generalization: Model Development and Validation Study.JMIR medical informatics · 2021Article
- Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA.Frontiers in public health · 2021Article
- Photosynthetic protein classification using genome neighborhood-based machine learning feature.Scientific reports · 2020Article
- Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.Frontiers in medicine · 2020Article
- HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets.Computational intelligence and neuroscience · 2020Article
- A semi-supervised machine learning framework for microRNA classification.Human genomics · 2019Article
- Multi-view Co-training for microRNA Prediction.Scientific reports · 2019Article
- Development of species specific putative miRNA and its target prediction tool in wheat (Triticum aestivum L.).Scientific reports · 2019Article
- Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks.BMC genomics · 2019Article
- miPIE: NGS-based Prediction of miRNA Using Integrated Evidence.Scientific reports · 2019Article
- Genome-Wide Identification of Putative MicroRNAs in Cassava (BioMed research international · 2019Article
- NetMiner-an ensemble pipeline for building genome-wide and high-quality gene co-expression network using massive-scale RNA-seq samples.PloS one · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs. These are applicable across different species. By applying preprocessing methods--both a correlation-based feature selection (CFS) with genetic algorithm (GA) search method and a modified-Synthetic Minority Oversampling Technique (SMOTE) bagging rebalancing method--improvement in the performance of this ensemble was observed. The overall prediction accuracies obtained via 10 runs of 5-fold cross validation (CV) was 96.54%, with sensitivity of 94.8% and specificity of 98.3%-this is better in trade-off sensitivity and specificity values than those of other state-of-the-art methods. The ensemble model was applied to animal, plant and virus pre-miRNA and achieved high accuracy, >93%. Exploiting the discriminative set of selected features also suggests that pre-miRNAs possess high intrinsic structural robustness as compared with other stem loops. Our heterogeneous ensemble method gave a relatively more reliable prediction than those using single classifiers. Our program is available at http://ncrna-pred.com/premiRNA.html.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.