ArticlePloS one2011
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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Who cites it
98 citing papers in PubMed, 289 citations in OpenAlex.
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- ProGraphTrans: multimodal dynamic collaborative framework for protein representation learning.BMC biology · 2025Article
- Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.PloS one · 2025Article
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning.Frontiers in genetics · 2024Article
- Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa).Journal, genetic engineering & biotechnology · 2023Article
- FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation.Interdisciplinary sciences, computational life sciences · 2022Article
- Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.Frontiers in pharmacology · 2022Article
- DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform.Computational intelligence and neuroscience · 2022Article
- Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins.Computational and mathematical methods in medicine · 2022Article
- Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance.Scientific reports · 2021Article
- UMAP-DBP: An Improved DNA-Binding Proteins Prediction Method Based on Uniform Manifold Approximation and Projection.The protein journal · 2021Article
- A sequence-based multiple kernel model for identifying DNA-binding proteins.BMC bioinformatics · 2021Article
- KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.Frontiers in genetics · 2021Article
- Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.Frontiers in genetics · 2021Review
- iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.PeerJ · 2021Article
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- Phosphoglycerate kinase: structural aspects and functions, with special emphasis on the enzyme from Kinetoplastea.Open biology · 2020Review
- Downy Mildew effector HaRxL21 interacts with the transcriptional repressor TOPLESS to promote pathogen susceptibility.PLoS pathogens · 2020Article
- HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2020Article
38 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
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power. By incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via the "grey model" and by adopting the random forest operation engine, we proposed a new predictor, called iDNA-Prot, for identifying uncharacterized proteins as DNA-binding proteins or non-DNA binding proteins based on their amino acid sequences information alone. The overall success rate by iDNA-Prot was 83.96% that was obtained via jackknife tests on a newly constructed stringent benchmark dataset in which none of the proteins included has ≥25% pairwise sequence identity to any other in a same subset. In addition to achieving high success rate, the computational time for iDNA-Prot is remarkably shorter in comparison with the relevant existing predictors. Hence it is anticipated that iDNA-Prot may become a useful high throughput tool for large-scale analysis of DNA-binding proteins. As a user-friendly web-server, iDNA-Prot is freely accessible to the public at the web-site on http://icpr.jci.edu.cn/bioinfo/iDNA-Prot or http://www.jci-bioinfo.cn/iDNA-Prot. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results.
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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.