ArticleInternational journal of molecular sciences2011
Prediction of lysine ubiquitylation with ensemble classifier and feature selection.
Article in International journal of molecular sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction.PeerJ · 2022Article
- Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.Frontiers in genetics · 2022Article
- UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites.Genes · 2021Article
- Computational Prediction of Ubiquitination Proteins Using Evolutionary Profiles and Functional Domain Annotation.Current genomics · 2019Article
- Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.BMC bioinformatics · 2019Article
- DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.BMC bioinformatics · 2019Article
- Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation.Frontiers in bioengineering and biotechnology · 2019Article
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.BMC bioinformatics · 2018Article
- A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.BioMed research international · 2018Article
- EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.BMC bioinformatics · 2017Article
- PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context.Scientific reports · 2016Article
- A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data.International journal of molecular sciences · 2016Article
- UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.BMC systems biology · 2016Article
- An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.International journal of molecular sciences · 2015Article
- Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.BMC systems biology · 2015Article
- Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities.BMC bioinformatics · 2015Article
- Position-specific analysis and prediction of protein pupylation sites based on multiple features.BioMed research international · 2013Article
- Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles.International journal of molecular sciences · 2012Article
- Prediction of protein phosphorylation sites by using the composition of k-spaced amino acid pairs.PloS one · 2012Article
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
Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request.
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.