scholarly journals Improved data sets and evaluation methods for the automatic prediction of DNA-binding proteins

2021 ◽  
Author(s):  
Alexander Zaitzeff ◽  
Nicholas Leiby ◽  
Francis C Motta ◽  
Steven Haase ◽  
Jedediah M Singer
Keyword(s):  
Dna Binding ◽  
Protein Function ◽  
Binding Proteins ◽  
Nearest Neighbor ◽  
Language Model ◽  
Dna Binding Proteins ◽  
Gradient Boosting ◽  
Data Sets ◽  
Binding Regions ◽  
And Control

Accurate automatic annotation of protein function relies on both innovative models and robust data sets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms.

Identification of DNA-binding proteins via Hypergraph based Laplacian Support Vector Machine

2021 ◽  
Vol 16 ◽  
Author(s):  
Yuqing Qian ◽  
Hao Meng ◽  
Weizhong Lu ◽  
Zhijun Liao ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Binding ◽  
Large Scale ◽  
Binding Proteins ◽  
Predictive Accuracy ◽  
Dna Binding Proteins ◽  
Research Field ◽  
Support Vector ◽  
Data Sets ◽  
Independent Test

Background: The identification of DNA binding proteins (DBP) is an important research field. Experiment-based methods are time-consuming and labor-intensive for detecting DBP. Objective: To solve the problem of large-scale DBP identification, some machine learning methods are proposed. However, these methods have insufficient predictive accuracy. Our aim is to develop a sequence-based machine learning model to predict DBP. Methods: In our study, we extract six types of features (including NMBAC, GE, MCD, PSSM-AB, PSSM-DWT, and PsePSSM) from protein sequences. We use Multiple Kernel Learning based on Hilbert-Schmidt Independence Criterion (MKL-HSIC) to estimate the optimal kernel. Then, we construct a hypergraph model to describe the relationship between labeled and unlabeled samples. Finally, Laplacian Support Vector Machines (LapSVM) is employed to train the predictive model. Our method is tested on PDB186, PDB1075, PDB2272 and PDB14189 data sets. Result: Compared with other methods, our model achieves best results on benchmark data sets. Conclusion: The accuracy of 87.1% and 74.2% are achieved on PDB186 (Independent test of PDB1075) and PDB2272 (Independent test of PDB14189), respectively.

scholarly journals HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xiuzhi Sang ◽  
Wanyue Xiao ◽  
Huiwen Zheng ◽  
Yang Yang ◽  
Taigang Liu
Keyword(s):  
Feature Selection ◽  
Dna Binding ◽  
Binding Proteins ◽  
Biological Activities ◽  
Dna Binding Proteins ◽  
Gradient Boosting ◽  
Support Vector ◽  
Svm Classifier ◽  
Cross Covariance ◽  
Extreme Gradient Boosting

Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto- and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

SUPPORT VECTOR MACHINE CLASSIFICATION OF PHYSICAL AND BIOLOGICAL DATASETS

2003 ◽  
Vol 14 (05) ◽  
pp. 575-585 ◽  
Author(s):  
CONG-ZHONG CAI ◽  
WAN-LU WANG ◽  
YU-ZONG CHEN
Keyword(s):  
Support Vector Machine ◽  
Dna Binding ◽  
Protein Interactions ◽  
Binding Proteins ◽  
Nearest Neighbor ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Testing Accuracy ◽  
Better Than

The support vector machine (SVM) is used in the classification of sonar signals and DNA-binding proteins. Our study on the classification of sonar signals shows that SVM produces a result better than that obtained from other classification methods, which is consistent from the findings of other studies. The testing accuracy of classification is 95.19% as compared with that of 90.4% from multilayered neural network and that of 82.7% from nearest neighbor classifier. From our results on the classification of DNA-binding proteins, one finds that SVM gives a testing accuracy of 82.32%, which is slightly better than that obtained from an earlier study of SVM classification of protein–protein interactions. Hence, our study indicates the usefulness of SVM in the identification of DNA-binding proteins. Further improvements in SVM algorithm and parameters are suggested.

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

2020 ◽  
Author(s):  
Qingmei Zhang ◽  
Peishun Liu ◽  
Yu Han ◽  
Yaqun Zhang ◽  
Xue Wang ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Ensemble Classifier ◽  
Dna Binding Proteins ◽  
Position Specific Scoring Matrix ◽  
Gradient Boosting ◽  
Feature Subset ◽  
Extreme Gradient Boosting ◽  
Independent Test ◽  
Scoring Matrix

ABSTRACTDNA 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.

PseKNC and Adaboost-Based Method for DNA-Binding Proteins Recognition

2021 ◽  
pp. 2150022
Author(s):  
Lina Yang ◽  
Xiangyu Li ◽  
Ting Shu ◽  
Patrick Wang ◽  
Xichun Li
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Nearest Neighbor ◽  
Dna Recombination ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Jackknife Test ◽  
Optimal Feature ◽  
Fold Cross Validation

DNA-binding proteins are an essential part of the DNA. It also an integral component during life processes of various organisms, for instance, DNA recombination, replication, and so on. Recognition of such proteins helps medical researchers pinpoint the cause of disease. Traditional techniques of identifying DNA-binding proteins are expensive and time-consuming. Machine learning methods can identify these proteins quickly and efficiently. However, the accuracies of the existing related methods were not high enough. In this paper, we propose a framework to identify DNA-binding proteins. The proposed framework first uses PseKNC (ps), MomoKGap (mo), and MomoDiKGap (md) methods to combine three algorithms to extract features. Further, we apply Adaboost weight ranking to select optimal feature subsets from the above three types of features. Based on the selected features, three algorithms (k-nearest neighbor (knn), Support Vector Machine (SVM), and Random Forest (RF)) are applied to classify it. Finally, three predictors for identifying DNA-binding proteins are established, including [Formula: see text], [Formula: see text], [Formula: see text]. We utilize benchmark and independent datasets to train and evaluate the proposed framework. Three tests are performed, including Jackknife test, 10-fold cross-validation and independent test. Among them, the accuracy of ps+md is the highest. We named the model with the best result as psmdDBPs and applied it to identify DNA-binding proteins.

Identification of DNA-Binding Proteins by Multiple Kernel Support Vector Machine and Sequence Information

2020 ◽  
Vol 17 (4) ◽  
pp. 302-310
Author(s):  
Yijie Ding ◽  
Feng Chen ◽  
Xiaoyi Guo ◽  
Jijun Tang ◽  
Hongjie Wu
Keyword(s):  
Support Vector Machine ◽  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Computational Method ◽  
Support Vector ◽  
Sequence Information ◽  
Data Sets ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine

Background: The DNA-binding proteins is an important process in multiple biomolecular functions. However, the tradition experimental methods for DNA-binding proteins identification are still time consuming and extremely expensive. Objective: In past several years, various computational methods have been developed to detect DNAbinding proteins. However, most of them do not integrate multiple information. Methods: In this study, we propose a novel computational method to predict DNA-binding proteins by two steps Multiple Kernel Support Vector Machine (MK-SVM) and sequence information. Firstly, we extract several feature and construct multiple kernels. Then, multiple kernels are linear combined by Multiple Kernel Learning (MKL). At last, a final SVM model, constructed by combined kernel, is built to predict DNA-binding proteins. Results: The proposed method is tested on two benchmark data sets. Compared with other existing method, our approach is comparable, even better than other methods on some data sets. Conclusion: We can conclude that MK-SVM is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

scholarly journals A sequence-based multiple kernel model for identifying DNA-binding proteins

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuqing Qian ◽  
Limin Jiang ◽  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Multiple Kernel Learning ◽  
Dna Binding Proteins ◽  
Kernel Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Data Sets ◽  
System A ◽  
Multiple Kernel

Abstract Background DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. Results In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ($$84.19\%$$ 84.19 % ) and PDB186 ($$83.7\%$$ 83.7 % ), respectively. Conclusion Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

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