TargetDBP: Accurate DNA-Binding Protein Prediction via Sequence-based Multi-View Feature Learning

Hu, Jun; Zhou, Xiaogen; Zhu, Yiheng; Yu, Dong‐Jun; Zhang, Guijun

doi:10.1109/tcbb.2019.2893634

Cited by 36 publications

(42 citation statements)

References 61 publications

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“…Ensembles generated from the methods described above are evaluated across four benchmark datasets taken from PDB1075 [8], PDB594 [67], PDB676 [68], PDB186 [67] (which is from the Protein Databank located at http://www.rcsb.org/pdb/home/home.do) and the dataset in [69]. Protein sequences in these datasets with less than 50 amino acids or that contain the character "X" were removed.…”

Section: Datasetsmentioning

confidence: 99%

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Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

Nanni

Brahnam

2021

ACI

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PurposeAutomatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.Design/methodology/approachEfficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.FindingsThe best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.Originality/valueMost DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

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Section: Datasetsmentioning

confidence: 99%

“…(4) IND4 [69]: training takes place on 2104 proteins and testing on 296 proteins. This new non-redundant gold-standard dataset was created following Chou's five-step rule [70].…”

Section: Datasetsmentioning

confidence: 99%

Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

Nanni

Brahnam

2021

ACI

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“…For each group, the sum of the elements in each column is calculated. In this way, each protein sequence can get an 20 20  RPT matrix, as shown in equation 13 13A 400-dimension row vector is obtained by expanding the RPT matrix, as shown in formula (14):…”

Section: Residue Probing Transformation (Rpt)mentioning

confidence: 99%

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

et al. 2020

Preprint

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DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

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“…Therefore, choosing an appropriate dimension reduction method is also an important step in the process of DBPs identification. Hu et al [14] fused four feature extraction methods of AAC, pseudo predicted relative solvent accessibility (PsePRSA), PsePSSM, and pseudo predicted probabilities of DNA-binding sites (PsePPDBS). Support vector machine recursive feature elimination and correlation bias reduction (SVM-RFE+CBR) [15] was used to convert the nonlinear learning issue in the original feature space to a linear learning issue in the high dimension feature space.…”

Section: Introductionmentioning

confidence: 99%

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

Zhang

Liu

Wang

et al. 2021

Applied Soft Computing

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DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research.Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075，93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive  Corresponding author.

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TargetDBP: Accurate DNA-Binding Protein Prediction via Sequence-based Multi-View Feature Learning

Cited by 36 publications

References 61 publications

Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

Robust ensemble of handcrafted and learned approaches for DNA-binding proteins

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

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