ReviewBioMed research international2017
A Review on Recent Computational Methods for Predicting Noncoding RNAs.
Review in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
23 citing papers in PubMed, 48 citations in OpenAlex.
- Copy number evolution of conserved noncoding RNA gene families across mammals.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- GRAMEP: an alignment-free method based on the maximum entropy principle for identifying SNPs.BMC bioinformatics · 2025Article
- Comparison and benchmark of deep learning methods for non-coding RNA classification.PLoS computational biology · 2024Article
- Transcriptomic analyses of differentially expressed human genes, micro RNAs and long-non-coding RNAs in severe, symptomatic and asymptomatic malaria infection.Scientific reports · 2024Article
- miRNA- and Cell Line-Specific Constraints on Precursor miRNA Processing of Stably Transfected Pancreatic Cancer and Other Mammalian Cells.International journal of molecular sciences · 2024Article
- ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction.Biomolecules · 2023Article
- MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts.BMC bioinformatics · 2022Article
- Prokaryotic ncRNAs: Master regulators of gene expression.Current research in pharmacology and drug discovery · 2022Article
- Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization.Frontiers in microbiology · 2022Article
- Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.Briefings in bioinformatics · 2021Article
- Elucidating the Functions of Non-Coding RNAs from the Perspective of RNA Modifications.Non-coding RNA · 2021Review
- Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review.Frontiers in oncology · 2021Review
- Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs.Methods in molecular biology (Clifton, N.J.) · 2021Article
- An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria.Frontiers in microbiology · 2021Article
- Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.Frontiers in genetics · 2021Article
- The Potential Diagnostic and Prognostic Value of Circulating MicroRNAs in the Assessment of Patients With Prostate Cancer: Rational and Progress.Frontiers in oncology · 2021Review
- Article
- Trends in the development of miRNA bioinformatics tools.Briefings in bioinformatics · 2019Review
- Genome-wide identification and characterization of long non-coding RNAs during postnatal development of rabbit adipose tissue.Lipids in health and disease · 2018Article
- Origin, Evolution, and Loss of Bacterial Small RNAs.Microbiology spectrum · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Noncoding RNAs (ncRNAs) play important roles in various cellular activities and diseases. In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and
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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.