ArticleBMC bioinformatics2014
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 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
74 citing papers in PubMed.
- Bayesian neural network with unified entropy source and synapse weights using 3D 16-layer Fe-diode array.Nature communications · 2025Article
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids.ACS omega · 2024Article
- Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks.Plant molecular biology · 2021Article
- Identifying Plant Pentatricopeptide Repeat Proteins Using a Variable Selection Method.Frontiers in plant science · 2021Article
- Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study.Journal of clinical medicine · 2020Article
- PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.Scientific reports · 2020Article
- DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network.Frontiers in cell and developmental biology · 2020Article
- A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.Frontiers in bioengineering and biotechnology · 2020Article
- Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
- Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.BMC genomics · 2019Article
- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models.International journal of molecular sciences · 2019Article
- An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.BMC bioinformatics · 2019Article
- MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.BMC bioinformatics · 2019Article
- Gene2vec: gene subsequence embedding for prediction of mammalianRNA (New York, N.Y.) · 2019Article
- PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.BMC bioinformatics · 2018Article
- SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.PLoS computational biology · 2018Article
- WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks.BMC bioinformatics · 2018Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.
resultsIn this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.
conclusionsOur method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.
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.