ArticleBioMed research international2014
enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 41 citations in OpenAlex.
- Accurate prediction of nucleic acid binding proteins using protein language model.Bioinformatics advances · 2025Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- Improved prediction of DNA and RNA binding proteins with deep learning models.Briefings in bioinformatics · 2024Article
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- Identifying Plant Pentatricopeptide Repeat Proteins Using a Variable Selection Method.Frontiers in plant science · 2021Article
- A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.Frontiers in bioengineering and biotechnology · 2020Article
- Using a Classifier Fusion Strategy to Identify Anti-angiogenic Peptides.Scientific reports · 2018Article
- Article
- Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods.Frontiers in plant science · 2018Article
- Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation.Combinatorial chemistry & high throughput screening · 2018Article
- Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.Molecules (Basel, Switzerland) · 2017Article
- EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.BMC bioinformatics · 2017Article
- Improved detection of DNA-binding proteins via compression technology on PSSM information.PloS one · 2017Article
- De-novo protein function prediction using DNA binding and RNA binding proteins as a test case.Nature communications · 2016Article
- Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.BMC bioinformatics · 2016Article
- PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context.Scientific reports · 2016Article
- DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.PloS one · 2016Article
- An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.International journal of molecular sciences · 2015Article
- nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.BMC bioinformatics · 2014Article
- Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers.Avicenna journal of medical biotechnologyArticle
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97-9.52% in ACC and 0.08-0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83-16.63% in terms of ACC and 0.02-0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public.
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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.