ArticleMolecular genetics and genomics : MGG2018
Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.
Article in Molecular genetics and genomics : MGG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 67 citations in OpenAlex.
- CircCNNs, a convolutional neural network framework to better understand the biogenesis of exonic circRNAs.Scientific reports · 2024Article
- CircRNA identification and feature interpretability analysis.BMC biology · 2024Article
- A Brief Review of circRNA Biogenesis, Detection, and Function.Current genomics · 2021Review
- Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA.Genes · 2021Article
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites.Bioinformatics (Oxford, England) · 2021Article
- The Key Genes for Perineural Invasion in Pancreatic Ductal Adenocarcinoma Identified With Monte-Carlo Feature Selection Method.Frontiers in genetics · 2020Article
- circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.Bioinformatics (Oxford, England) · 2020Article
- The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer.Frontiers in genetics · 2020Article
- CirRNAPL: A web server for the identification of circRNA based on extreme learning machine.Computational and structural biotechnology journal · 2020Article
- Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.Frontiers in physiology · 2020Article
- Identifying Circular RNA and Predicting Its Regulatory Interactions by Machine Learning.Frontiers in genetics · 2020Article
- RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization.Frontiers in microbiology · 2020Article
- The Methylation Pattern for Knee and Hip Osteoarthritis.Frontiers in cell and developmental biology · 2020Article
- Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes.International journal of molecular sciences · 2019Article
- Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.International journal of molecular sciences · 2019Article
- Identification of the copy number variant biomarkers for breast cancer subtypes.Molecular genetics and genomics : MGG · 2019Article
- HIV infection alters the human epigenetic landscape.Gene therapy · 2019Article
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Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques. In this study, we investigated these two types of non-coding RNAs using several computational methods. The purpose was to extract important factors that could distinguish cirRNAs from other lncRNAs and build an effective classification model to distinguish them. First, we collected cirRNAs, lncRNAs and their representations from a previous study, in which each cirRNA or lncRNA was represented by 188 features derived from its graph representation, sequence and conservation properties. Second, these features were analyzed by the minimum redundancy maximum relevance (mRMR) method. The obtained mRMR feature list, incremental feature selection method and hierarchical extreme learning machine algorithm were employed to build an optimal classification model with sensitivity of 0.703, specificity of 0.850, accuracy of 0.789 and a Matthews correlation coefficient of 0.561. Finally, we analyzed the 16 most important features. Of them, the sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs. Meanwhile, other features of evolutionary conversation, sequence consecution were also important.
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Registered trials
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.