ArticleJournal of nucleic acids2012
Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees.
Article in Journal of nucleic acids, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 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
12 citing papers in PubMed.
- Bibliometric-Based Analysis of Global Trends and Collaborative Networks in Plant Genetic Engineering (1994-2024).Plant biotechnology journal · 2026Review
- Article
- Modern Approaches for Transcriptome Analyses in Plants.Advances in experimental medicine and biology · 2021Article
- Article
- Computational tools for plant small RNA detection and categorization.Briefings in bioinformatics · 2019Review
- Computational methods for the ab initio identification of novel microRNA in plants: a systematic review.PeerJ. Computer science · 2019Article
- Corrigendum to "Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees".Journal of nucleic acids · 2017Article
- A Comprehensive Prescription for Plant miRNA Identification.Frontiers in plant science · 2016Article
- ptRNApred: computational identification and classification of post-transcriptional RNA.Nucleic acids research · 2014Article
- Plant miRNAome and antiviral resistance: a retrospective view and prospective challenges.Virus genes · 2014Review
- Demystifying animal models of adverse pregnancy outcomes: touching bench and bedside.American journal of reproductive immunology (New York, N.Y. : 1989) · 2013Article
- A Review of Computational Tools in microRNA Discovery.Frontiers in genetics · 2013Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require "read count" to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA(∗) duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.
Identifiers
What OpenQuestion 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 OpenQuestion graph.