ArticleNucleic acids research2014
Identification of non-coding RNAs with a new composite feature in the Hybrid Random Forest Ensemble algorithm.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- On the Completeness of Existing RNA Fragment Structures.Genomics, proteomics & bioinformatics · 2025Article
- Identification of a hypoxia-suppressed lncRNA RAMP2-AS1 in breast cancer.Non-coding RNA research · 2024Article
- MFPINC: prediction of plant ncRNAs based on multi-source feature fusion.BMC genomics · 2024Article
- A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug-Target Interaction Prediction.Molecules (Basel, Switzerland) · 2023Article
- Article
- CircPCBL: Identification of Plant CircRNAs with a CNN-BiGRU-GLT Model.Plants (Basel, Switzerland) · 2023Article
- PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework.International journal of molecular sciences · 2022Article
- mSRFR: a machine learning model using microalgal signature features for ncRNA classification.BioData mining · 2022Article
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- Inferring Potential CircRNA-Disease Associations via Deep Autoencoder-Based Classification.Molecular diagnosis & therapy · 2021Article
- A classification model for lncRNA and mRNA based on k-mers and a convolutional neural network.BMC bioinformatics · 2019Article
- CPPred: coding potential prediction based on the global description of RNA sequence.Nucleic acids research · 2019Article
- Genome-Wide Identification of Putative MicroRNAs in Cassava (BioMed research international · 2019Article
- IRSOM, a reliable identifier of ncRNAs based on supervised self-organizing maps with rejection.Bioinformatics (Oxford, England) · 2018Article
- FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome.Nucleic acids research · 2017Article
- An improved method for identification of small non-coding RNAs in bacteria using support vector machine.Scientific reports · 2017Article
- nRC: non-coding RNA Classifier based on structural features.BioData mining · 2017Article
- Computational identification of putative lincRNAs in mouse embryonic stem cell.Scientific reports · 2016Article
- Long non-coding RNAs and their biological roles in plants.Genomics, proteomics & bioinformatics · 2015Review
- Dietary MicroRNA Database (DMD): An Archive Database and Analytic Tool for Food-Borne microRNAs.PloS one · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To identify non-coding RNA (ncRNA) signals within genomic regions, a classification tool was developed based on a hybrid random forest (RF) with a logistic regression model to efficiently discriminate short ncRNA sequences as well as long complex ncRNA sequences. This RF-based classifier was trained on a well-balanced dataset with a discriminative set of features and achieved an accuracy, sensitivity and specificity of 92.11%, 90.7% and 93.5%, respectively. The selected feature set includes a new proposed feature, SCORE. This feature is generated based on a logistic regression function that combines five significant features-structure, sequence, modularity, structural robustness and coding potential-to enable improved characterization of long ncRNA (lncRNA) elements. The use of SCORE improved the performance of the RF-based classifier in the identification of Rfam lncRNA families. A genome-wide ncRNA classification framework was applied to a wide variety of organisms, with an emphasis on those of economic, social, public health, environmental and agricultural significance, such as various bacteria genomes, the Arthrospira (Spirulina) genome, and rice and human genomic regions. Our framework was able to identify known ncRNAs with sensitivities of greater than 90% and 77.7% for prokaryotic and eukaryotic sequences, respectively. Our classifier is available at http://ncrna-pred.com/HLRF.htm.
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