ArticleScientific reports2016
iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Article
- Article
- Article
- Computational Detection of Pre-microRNAs.Methods in molecular biology (Clifton, N.J.) · 2022Review
- The Potential Diagnostic and Prognostic Value of Circulating MicroRNAs in the Assessment of Patients With Prostate Cancer: Rational and Progress.Frontiers in oncology · 2021Review
- Epigenetic Regulator miRNA Pattern Differences Among SARS-CoV, SARS-CoV-2, and SARS-CoV-2 World-Wide Isolates Delineated the Mystery Behind the Epic Pathogenicity and Distinct Clinical Characteristics of Pandemic COVID-19.Frontiers in genetics · 2020Article
- iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition.Frontiers in genetics · 2019Article
- Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures.Frontiers in genetics · 2019Article
- k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification.Frontiers in genetics · 2019Article
- New 3D graphical representation for RNA structure analysis and its application in the pre-miRNA identification of plants.RSC advances · 2018Article
- Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.Scientific reports · 2018Article
- SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.BMC genomics · 2017Article
- On the performance of pre-microRNA detection algorithms.Nature communications · 2017Article
- An improved method for identification of small non-coding RNAs in bacteria using support vector machine.Scientific reports · 2017Article
- Chemical property based sequence characterization of PpcA and its homolog proteins PpcB-E: A mathematical approach.PloS one · 2017Article
- Article
- Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.BMC systems biology · 2016Article
- Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis.Oncotarget · 2016Article
- iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance.Scientific reports · 2016Article
- dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of microRNA precursors (pre-miRNAs) helps in understanding regulator in biological processes. The performance of computational predictors depends on their training sets, in which the negative sets play an important role. In this regard, we investigated the influence of benchmark datasets on the predictive performance of computational predictors in the field of miRNA identification, and found that the negative samples have significant impact on the predictive results of various methods. We constructed a new benchmark set with different data distributions of negative samples. Trained with this high quality benchmark dataset, a new computational predictor called iMiRNA-SSF was proposed, which employed various features extracted from RNA sequences. Experimental results showed that iMiRNA-SSF outperforms three state-of-the-art computational methods. For practical applications, a web-server of iMiRNA-SSF was established at the website http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/.
Indexed as
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.