ArticleFunctional & integrative genomics2021
LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants.
Article in Functional & integrative genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Unveiling the potential regulatory functions of long non-coding RNAs in potato tuberization through integrated analysis of transcriptomics and phytohormone profiles.BMC plant biology · 2026Article
- Transcriptome-wide identification and expression analysis of lncRNAs involved in the nitrate response in wheat.BMC plant biology · 2025Article
- PlantLncBoost: key features for plant lncRNA identification and significant improvement in accuracy and generalization.The New phytologist · 2025Article
- Positional frequency chaos game representation for machine learning-based classification of crop lncRNAs.bioRxiv : the preprint server for biology · 2025Article
- Role of non-coding RNAs in quality improvement of horticultural crops: computational tools, databases, and algorithms for identification and analysis.Functional & integrative genomics · 2025Review
- The miR3367-lncRNA67-GhCYP724B module regulates male sterility by modulating brassinosteroid biosynthesis and interacting with Aorf27 in Gossypium hirsutum.Journal of integrative plant biology · 2025Article
- Computational Resources for lncRNA Functions and Targetome.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Transcriptomic Complexity of Culm Growth and Development in Different Types of Moso Bamboo.International journal of molecular sciences · 2023Review
- Genome-Wide Analysis of Long Non-Coding RNAs Related to UV-B Radiation in the Antarctic MossInternational journal of molecular sciences · 2023Article
- Review
- DNABERT-based explainable lncRNA identification in plant genome assemblies.Computational and structural biotechnology journal · 2023Article
- A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.Nucleic acids research · 2022Article
- Advances in Non-Coding RNA Sequencing.Non-coding RNA · 2021Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.
Indexed as
Identifiers
33635499What Socratic holds
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 Socratic graph.