ArticleScientific reports2015
DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 128 citations in OpenAlex.
- DRBP-EDP: classification of DNA-binding proteins and RNA-binding proteins using ESM-2 and dual-path neural network.NAR genomics and bioinformatics · 2025Article
- Systematic discovery of DNA-binding tandem repeat proteins.Nucleic acids research · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- 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
- FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation.Interdisciplinary sciences, computational life sciences · 2022Article
- Roles of Physicochemical and Structural Properties of RNA-Binding Proteins in Predicting the Activities of Trans-Acting Splicing Factors with Machine Learning.International journal of molecular sciences · 2022Article
- Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.Frontiers in pharmacology · 2022Article
- Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins.Computational and mathematical methods in medicine · 2022Article
- Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.Computational and mathematical methods in medicine · 2022Article
- 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
- Amino Acid Composition in Various Types of Nucleic Acid-Binding Proteins.International journal of molecular sciences · 2021Review
- 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
- HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2020Article
- Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.BioMed research international · 2020Article
- PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification, and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.
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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.