ArticleGenes2019
PredLnc-GFStack: A Global Sequence Feature Based on a Stacked Ensemble Learning Method for Predicting lncRNAs from Transcripts.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
The trial behind it
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
9 citing papers in PubMed.
- Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration.ACS pharmacology & translational science · 2024Review
- RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.Nucleic acids research · 2023Article
- Computational prediction of disease related lncRNAs using machine learning.Scientific reports · 2023Article
- A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.Nucleic acids research · 2022Article
- Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.Frontiers in plant science · 2022Article
- Common Features in lncRNA Annotation and Classification: A Survey.Non-coding RNA · 2021Review
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol.Frontiers in bioengineering and biotechnology · 2020Article
- The computational approaches of lncRNA identification based on coding potential:Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding RNAs (lncRNAs) are a class of RNAs with the length exceeding 200 base pairs (bps), which do not encode proteins, nevertheless, lncRNAs have many vital biological functions. A large number of novel transcripts were discovered as a result of the development of high-throughput sequencing technology. Under this circumstance, computational methods for lncRNA prediction are in great demand. In this paper, we consider global sequence features and propose a stacked ensemble learning-based method to predict lncRNAs from transcripts, abbreviated as PredLnc-GFStack. We extract the critical features from the candidate feature list using the genetic algorithm (GA) and then employ the stacked ensemble learning method to construct PredLnc-GFStack model. Computational experimental results show that PredLnc-GFStack outperforms several state-of-the-art methods for lncRNA prediction. Furthermore, PredLnc-GFStack demonstrates an outstanding ability for cross-species ncRNA prediction.
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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.