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PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.
| Content Provider | Europe PMC |
|---|---|
| Author | Wang, Jun Zheng, Huiwen Yang, Yang Xiao, Wanyue Liu, Taigang |
| Copyright Year | 2020 |
| Abstract | DNA-binding proteins (DBPs) play vital roles in all aspects of genetic activities. However, the identification of DBPs by using wet-lab experimental approaches is often time-consuming and laborious. In this study, we develop a novel computational method, called PredDBP-Stack, to predict DBPs solely based on protein sequences. First, amino acid composition (AAC) and transition probability composition (TPC) extracted from the hidden markov model (HMM) profile are adopted to represent a protein. Next, we establish a stacked ensemble model to identify DBPs, which involves two stages of learning. In the first stage, the four base classifiers are trained with the features of HMM-based compositions. In the second stage, the prediction probabilities of these base classifiers are used as inputs to the meta-classifier to perform the final prediction of DBPs. Based on the PDB1075 benchmark dataset, we conduct a jackknife cross validation with the proposed PredDBP-Stack predictor and obtain a balanced sensitivity and specificity of 92.47% and 92.36%, respectively. This outcome outperforms most of the existing classifiers. Furthermore, our method also achieves superior performance and model robustness on the PDB186 independent dataset. This demonstrates that the PredDBP-Stack is an effective classifier for accurately identifying DBPs based on protein sequence information alone. |
| ISSN | 23146133 |
| Journal | Biomed Research International |
| Volume Number | 2020 |
| PubMed Central reference number | PMC7174956 |
| PubMed reference number | 32352006 |
| e-ISSN | 23146141 |
| DOI | 10.1155/2020/7297631 |
| Language | English |
| Publisher | Hindawi |
| Publisher Date | 2020-04-13 |
| Access Restriction | Open |
| Rights License | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2020 Jun Wang et al. |
| Content Type | Text |
| Resource Type | Article |
| Subject | Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology |
