ArticleNucleic acids research2009
Identification and classification of ncRNA molecules using graph properties.
Article in Nucleic acids research, 2009. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 57 citations in OpenAlex.
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- Antisense and Functional Nucleic Acids in Rational Drug Development.Antibiotics (Basel, Switzerland) · 2024Review
- ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction.Biomolecules · 2023Article
- High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information.Biomedicines · 2023Article
- MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding.International journal of molecular sciences · 2023Article
- ncDENSE: a novel computational method based on a deep learning framework for non-coding RNAs family prediction.BMC bioinformatics · 2023Article
- RNAsmc: A integrated tool for comparing RNA secondary structure and evaluating allosteric effects.Computational and structural biotechnology journal · 2023Article
- Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.Briefings in bioinformatics · 2021Article
- Thermostability, Tunability, and Tenacity of RNA as Rubbery Anionic Polymeric Materials in Nanotechnology and Nanomedicine-Specific Cancer Targeting with Undetectable Toxicity.Chemical reviews · 2021Review
- Deep learning predicts short non-coding RNA functions from only raw sequence data.PLoS computational biology · 2020Article
- The computational approaches of lncRNA identification based on coding potential:Computational and structural biotechnology journal · 2020Review
- Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs.BMC bioinformatics · 2018Article
- Prediction of plant lncRNA by ensemble machine learning classifiers.BMC genomics · 2018Article
- nRC: non-coding RNA Classifier based on structural features.BioData mining · 2017Article
- Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination.BioMed research international · 2016Review
- Prediction and classification of ncRNAs using structural information.BMC genomics · 2014Article
- BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms.Advances in bioinformatics · 2012Article
- Functional Annotation of Small Noncoding RNAs Target Genes Provides Evidence for a Deregulated Ubiquitin-Proteasome Pathway in Spinocerebellar Ataxia Type 1.Journal of nucleic acids · 2012Article
- Computational approaches to RNA structure prediction, analysis, and design.Current opinion in structural biology · 2011Review
- Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure.BMC bioinformatics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.
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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.