ArticlePloS one2017
Improved detection of DNA-binding proteins via compression technology on PSSM information.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- ProtDML: label-aware representation learning for broad-spectrum protein function prediction.Briefings in bioinformatics · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.Scientific reports · 2024Article
- GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.BMC genomics · 2023Article
- Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map.BioMed research international · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- A method for identifying moonlighting proteins based on linear discriminant analysis and bagging-SVM.Frontiers in genetics · 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
- A sequence-based multiple kernel model for identifying DNA-binding proteins.BMC bioinformatics · 2021Article
- AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models.Entropy (Basel, Switzerland) · 2021Article
- Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.Frontiers in genetics · 2021Article
- Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties.Frontiers in genetics · 2021Article
- iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2021Article
- VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost.Frontiers in genetics · 2021Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.Frontiers in genetics · 2020Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
- PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.BioMed research international · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix-Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix-Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.
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