ArticleJournal of biomedicine & biotechnology2011
Prediction of RNA-binding proteins by voting systems.
Article in Journal of biomedicine & biotechnology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 24 citations in OpenAlex.
- RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.Briefings in bioinformatics · 2022Article
- Prognosis and Early Diagnosis of Ductal and Lobular Type in Breast Cancer Patient.Iranian journal of public health · 2017Article
- Computational Prediction of RNA-Binding Proteins and Binding Sites.International journal of molecular sciences · 2015Review
- Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection.BioMed research international · 2015Article
- Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.BioMed research international · 2015Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
- Prediction of RNA binding proteins comes of age from low resolution to high resolution.Molecular bioSystems · 2013Review
- Identification of DNA-binding proteins using support vector machine with sequence information.Computational and mathematical methods in medicine · 2013Article
- Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG.BioMed research international · 2013Article
- Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers.Avicenna journal of medical biotechnologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.
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.