Effective DNA binding protein prediction by using key features via Chou’s general PseAAC

Adilina, Sheikh; Farid, Dewan Md.; Shatabda, Swakkhar

doi:10.1016/j.jtbi.2018.10.027

Cited by 49 publications

(26 citation statements)

References 62 publications

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“…In addition, in order to further verify our model, we used independent test samples in literatures [14,16,17,19] to test our model. Four methods are used to estimate the performance of our method, including Accuracy, Recall, Specificity, and MCC (Mathew’s correlation coefficient).…”

Section: Resultsmentioning

confidence: 99%

“…Adilinaet al improved the method for extracting sequence features by adopting grouping and recurrent selection to process the obtained feature sets. Their approach reduced the overfitting degree of the model [19]. With the development of network technology, some web implementations for discriminating DNA-binding proteins have been created that can provide online predictive services.…”

Section: Introductionmentioning

confidence: 99%

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An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences

Wang

2019

PLoS ONE

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As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional models. The experimental results show that the CNN-BiLSTM achieves a validation set prediction accuracy of 96.5%—7.8% higher than that of SVM, 9.6% higher than that of DNABP and 3.7% higher than that of CNN-RNN. After testing on 20,000 independent samples provided by UniProt that were not involved in model training, the accuracy of CNN-BiLSTM reached 94.5%—12% higher than that of SVM, 4.9% higher than that of DNABP and 4% higher than that of CNN-RNN. We visualized and compared the model training process of CNN-BiLSTM with that of CNN-RNN and found that the former is capable of better generalization from the training dataset, showing that CNN-BiLSTM has a wider range of adaptations to protein sequences. On the test set, CNN-BiLSTM has better credibility because its predicted scores are closer to the sample labels than are those of CNN-RNN. Therefore, the proposed CNN-BiLSTM is a more powerful method for identifying DNA-binding proteins.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences

Wang

2019

PLoS ONE

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“…Table 3 lists the results of comparison between our method and other methods. PseDNA-Pro [26], IDNA-Prot|dis [29], IDNA-Prot [43], DNAbinder [27], DNA-Prot [44], iDNAPro-PseAAC [45], Local-DPP [30], Adilina’s work [46] and Kmer1+ACC [47] are benchmark methods. And IDNA-Prot|dis (MCC: 0.54), PseDNA-Pro (MCC: 0.53) iDNAPro-PseAAC (MCC: 0.53) and Local-DPP (MCC: 0.59) obtain better performance.…”

Section: Resultsmentioning

confidence: 99%

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou’s Five-Step Rule

Zou

Ding

Tang

et al. 2019

IJMS

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DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm is employed to combine multiple features. Finally, a Fuzzy Kernel Ridge Regression (FKRR) model is built to detect DNA-binding proteins. Compared with other methods, our model achieves good results. Our method obtains an accuracy of 83.26% and 81.72% on two benchmark datasets (PDB1075 and compared with PDB186), respectively.

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“…The existing SVM-based predictive methods differ in encoding schemes for protein sequences. A great number of sequence features have been applied to represent protein sequences into fixed-length numeric vectors, such as amino acid composition (AAC) [17], dipeptide composition [18], pseudo-AAC [19][20][21][22], position-specific score matrix (PSSM) profile [23][24][25][26][27], predicted secondary structure [28], and hidden Markov model (HMM) profile [29].…”

Section: Introductionmentioning

confidence: 99%

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

Sang

Xiao

Zheng

et al. 2020

Computational and Mathematical Methods in Medicine

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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.

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Effective DNA binding protein prediction by using key features via Chou’s general PseAAC

Cited by 49 publications

References 62 publications

An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences

An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences

FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou’s Five-Step Rule

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

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