AJMB

Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers

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PMID: 30800250 (PubMed) - PMCID: PMC6359699 - View online: PubReader
Volume 11, Issue 1, January-March , Page 104 to 111
Wednesday, March 29, 2017 :Received , Wednesday, November 1, 2017 :Accepted

  • Corresponding author Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran, Tel: +98 21 82883404, Fax: +98 21 82884457, E-mail: Parviz@modares.ac.ir, m.poursheikh@gmail.com
    - Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

Abstract:

Background: Nucleic acid-binding proteins play major roles in different biological processes, such as transcription, splicing and translation. Therefore, the nucleic acid-binding function prediction of proteins is a step toward full functional annotation of proteins. The aim of our research was the improvement of nucleic-acid binding function prediction.
Methods: In the current study, nine machine-learning algorithms were used to predict RNA- and DNA-binding proteins and also to discriminate between RNA-binding proteins and DNA-binding proteins. The electrostatic features were utilized for prediction of each function in corresponding adapted protein datasets. The leave-one-out cross-validation process was used to measure the performance of employed classifiers.
Results: Radial basis function classifier gave the best results in predicting RNA- and DNA-binding proteins in comparison with other classifiers applied. In discriminating between RNA- and DNA-binding proteins, multilayer perceptron classifier was the best one.
Conclusion: Our findings show that the prediction of nucleic acid-binding function based on these simple electrostatic features can be improved by applied classifiers. Moreover, a reasonable progress to distinguish between RNA- and DNA-binding proteins has been achieved.


 

 

Introduction :

Protein-RNA interactions play a fundamental role in different bioprocesses, such as transcription, splicing and translation. RNA-Binding Proteins (RBPs) are pivotal ingredients of RNA splicing, export, stability, localization and translation. They organize all aspects of RNA biogenesis from maturation, surveillance, nucleocytoplasmic transfer to subcellular localization, translation and decomposition 1-3. Therefore, a full comprehension of a diverse variety of cellular processes requires the identification of RBPs.
So far, various computational methods have been developed for the identification of RBPs. Some of them have been based on sequence-derived features such as amino acid composition, dipeptide composition, composition-transition-distribution of seven physicochemical properties, evolutionary information in terms of position-specific scoring matrices and functional domain composition 4-17. The majority of sequence-based methods used the Support Vector Machine (SVM) algorithm for identifying RBPs. On the other hand, alternative methods have utilized the electrostatic features of the protein surface patches in order to identify RBPs 18,19. Moreover, there is a number of structural alignment and fold recognition approaches to tackle with this task 20-24. However, these structure-based methods have a small-scale usage because of limited known structures of proteins. Until now, there are three review papers that have focused specifically and comprehensively on RBPs identification methods 25-27.
Similar to RBPs, DNA-Binding Proteins (DBPs) have been predicted by using two different approaches. In the structure-based approach, the structural alignment and threading-based methods as well as the electrostatic features of the surface patches of proteins have been utilized for identifying DBPs 19,28-36. The electrostatic features have been used in this approach since large positively charged surface patches of proteins usually participate in interaction with DNA molecule. The second approach is the prediction of DBPs based on the sequence information 7,37-52. Numerous machine-learning algorithms have been built based on different encoding schemes of the protein sequence to predict DBPs, in this approach.
Machine-learning algorithms are extensively used to predict the structure and function of proteins 27. Actually, machine learning presents one of the most robust approaches to constructing predictive models in settings where experimentally validated training data are available. At present, however, it is unclear whether the available experimental data regarding DNA-protein and RNA-protein interactions are sufficient for successfully training classifiers using machine learning algorithms 25. Against this fact, this study applies machine learning approaches to train electrostatic-based classifiers for predicting DBPs and RBPs.
This study was done using simple electrostatic features including charge, dipole and quadruple moments for predicting RNA- and DNA-binding proteins by means of neural network method 53. Electrostatic interactions are among the most significant indicators to be considered when one will determine the function of proteins. It is now generally recognized that one must analyze the electrostatic forces in a protein to understand its function 54.
Here, this electrostatic-based approach was extended by applying 9 various machine learning classifiers for identifying RNA- and DNA-binding proteins and also for discriminating RBPs from DBPs. Our goal was to improve the classification accuracy in each of the comparisons. The results demonstrate that this approach can be used by other researchers in this field for more accurate nucleic acid-binding function prediction.

 

Materials and Methods :

Datasets: The protein datasets of Ahmad and Sarai 53 were used for our analysis. These datasets consisted of 160 RBP chains, 143 DBP chains and 2441 non nucleic acid-binding protein (Ctrl) chains.
Electrostatic features: The extracted electrostatic features of Ahmad and Sarai work 53 were used in the current study. These features included the charge, dipole and quadruple moments of protein chains. The detailed description of these features can be found at Ahmad S et al 53.
Machine learning algorithms: In this study, nine classification algorithms of Alternating Decision Tree (ADTree), K-nearest neighbor (K-NN), L1 Regularized Logistic Regression (L1 RLR), L2 Regularized Logistic Regression (L2 RLR), Multilayer Perceptron classifier (MLPClassifier), Random Forest (RF), Radial Basis Function classifier (RBFClassifier), RealAdaBoost and Sequential Minimal Optimization (SMO) algorithm were used to predict the nucleic acid-binding function of proteins (i.e., RNA- and DNA-binding) and also to differentiate between RBPs and DBPs. Waikato Environment for Knowledge Analysis (WEKA) package version 3.7.10, an ensemble of machine learning algorithms, was used to perform classifying tasks 55. Below, there is a brief explanation of nine employed classifiers and their corresponding parameter values:
ADTree: It generates an alternating decision tree 55. 20 boosting iterations were used in our experiments. All other parameters of the algorithm were set to default. Our classification was done with ADTree function of WEKA.
K-NN: It is a standard non-parametric classification method 55. The basic idea of the K-NN method is that a new case will be classified as the most frequent class among its K-Nearest Neighbors 55. K-NN was used with Euclidean distance and distance weighting (1/distance) and also 75 values of K ranging from K=1 to K=75 were examined and the best ones were selected in terms of the area under receiver operating characteristic curve (AUC) measure. All other parameters of the algorithm were set to default. IBk function of WEKA was used for classification.
L1 RLR and L2 RLR: Logistic regression is a well-established method for the classification or prediction of binary response function based on the various independent features. The regression utilizes an objective function and the number of its parameters is as large as the number of features. Usually the objective function contains also a regularization term. It penalizes model details of unnecessary complexity, focuses on the most concerned features, and thus avoids over-fitting of the data used for training (parameter optimization). The most common variants of regularization methods are L1 regularization, also known as Lasso and L2 regularization also known as ridge regression. As penalty term, the L1 regularization adds the sum of the absolute values of the model parameters to the objective function, whereas the L2 regularization adds the sum of squares of parameters 56.
LibLINEAR function of WEKA was utilized for doing prediction with these algorithms. The parameter cost (c) was changed from value 1 to 10 and the best one for our classifications was selected. The features were normalized and probability estimates for classification problems were generated. All other parameters of the algorithm were set to default.
MLPClassifier: It trains a multilayer perceptron with one hidden layer 55. MLPClassifier function was used and the number of units was changed in the hidden layer and the best architecture for our classifications was selected. All other parameters of the algorithm were set to default.
RF: It is an ensemble classifier method based on decision trees 55. After a large number of trees are generated, each tree in the forest gives a vote for a class and the most popular class among trees for a test instance presents the final classification. A few parameters influence the performance of RF models, such as the number of trees in the forest (ntree) and the number of variables considered at each split (mtry). RandomForest function was used in this study and 500 trees were grown in each experiment. For the number of variables randomly selected at each node, the default value that was equal to the square root of the feature dimension was used. Random forests were trained with a maximum depth of 30 trees.
RBFClassifier: It implements radial basis function networks for classification, trained in a fully supervised manner 55. RBFClassifier function was utilized and different numbers of base function were examined and the best one for classification was selected. All other parameters of the algorithm were set to default.
Real AdaBoost: It boosts a two-class classifier using the Real Adaboost method 55. The default parameter values of RealAdaBoost function were used for our classifications.
SMO: It is an implementation of SVM algorithm that globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes by default. SMO is conceptually simple, easy to implement and faster in computation. Fitting logistic regression models to the outputs of the SMO could in addition provide probability estimates 55.
SMO function was used and linear, polynomial (of degree 2) and radial basis function kernels in our classifications were examined and the best ones were selected. The cost factor c was appropriately chosen during the training time. All other parameters of the SMO algorithm were set to default.
Leave-One-Out Cross-Validation (LOOCV): The performance of our models trained on Ahmad and Sarai datasets 53 was assessed using LOOCV. In this procedure, one sample was taken out of the whole dataset and was used as the test instance, and the remaining samples were used as training instances. Then, the prediction was made for the test sample. This process was repeated n times (n=total number of samples), and the final performance results were obtained by averaging over all the test results. The sample can be a protein sequence, a protein chain, a DNA sequence, and so on. In this study, the sample was the protein chain. There is not any biological reason for picking this strategy. This is a statistical procedure.
Performance measures: Various performance measures were used to evaluate the results. These include accuracy, precision, recall, f-measure and the area under Receiver Operating Characteristic (ROC) curve (known as AUC). Accuracy shows proximity of measurement of results to the true value. It can be calculated as [(TP +TN)/(TP+TN+FP+FN)], where T refers to true and F refers to false, whereas P is positive class and N is negative class. Recall [(TP)/(TP+FN)] relates to the classifier’s ability to identify positive instances while precision [(TP)/(TP+FP)] is the fraction of predicted instances as positive class that is correctly predicted. F-measure [(2*precision*recall)/(precision+recall)] is the geometric mean of precision and recall. Also, models based on the ROC curve were evaluated which plot the true positive rate against false positive rate. The AUC value reported by an ROC curve is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. The AUC is a standard non threshold-dependent index for performance evaluation 57. ROCR library 58 of the R software 59 version 3.0.1 was used for obtaining numerical values of above-mentioned measures and also drawing ROC curves of different comparisons.

 

Results :

RBP chains versus Ctrl chains: Table 1 shows the obtained performance measures of nine employed classification algorithms for differentiation between RBP chains and Ctrl chains, in a LOOCV analysis. The RBFClassifier gave the best classification with the AUC value of 0.850. After that, the K-NN algorithm was the second most robust classifier.
DBP chains versus Ctrl chains: Table 2 demonstrates the obtained performance measures of nine employed classification algorithms for differentiation between DBP chains and Ctrl chains, in a LOOCV procedure. In the current dataset (i.e., DBP and Ctrl chains), the RBFClassifier again presented the best value for AUC (0.852) and then was selected as the best predictor. The MLPClassifer algorithm was the second best classifier with the AUC value of 0.846.
RBP chains versus DBP chains: Finally, table 3 presents the obtained performance measures of nine employed classification algorithms for differentiation between RBP chains and DBP chains, in a LOOCV process. In this dataset, the MLPClassifer algorithm reached to the value of 0.650 for the AUC that was the highest among other employed algorithms. The RBFClassifer algorithm was the latter in terms of AUC value (0.615).
ROC curves: For better comparison of applied machine-learning algorithms on 3 datasets, their corresponding ROC curves were depicted (Figures 1-3).
Comparison with other methods: The results of this research are comparable with the results of Ahmad and Sarai 53, because their protein datasets and also their extracted features were used in this study. The difference between their work and the current study is utilizing nine machine-learning algorithms other than the neural network they employed.
Obtained results in this study show improvement in the performance measures, especially in the AUC measure, when comparing with their work. Ahmad and Sarai reached to the AUC value of 0.78 for RBPs versus ctrl dataset, while this measure improved by 0.07 by means of RBFClassifier algorithm in the current study. Likewise, the accuracy was improved by 0.03 and reached to the 0.94. Another algorithm which improved these measures was K-NN which raised the AUC and accuracy values up to 0.84 and 0.94, respectively. Among 9 machine-learning algorithms employed on this dataset, only SMO algorithm had the AUC value less than neural network method. However, this algorithm had the accuracy value (0.94) more than that of Ahmad and Sarai method.
In the second dataset, i.e., DBPs vs ctrl, all of the nine utilized machine-learning algorithms yielded the AUC values more than the ones in Ahmad and Sarai work. The AUC value was interestingly improved by 0.13 and reached to the value of 0.85 by means of RBFClassifier, in comparison with the AUC value of their method (0.72). Also, the accuracy value was improved by all algorithms.
In the third comparison, i.e., RBPs vs. DBPs, the AUC value was raised to 0.65 by MLPClassifer and showed an increase by 0.07 compared to their obtained value (0.58). The precision measure was improved by the ADTree algorithm and reached to the value of 0.61, but the AUC value of this algorithm (0.575) was less than that of neural network method (0.58).

 

Discussion :

In this study, 9 machine-learning algorithms were used for discrimination between RBPs and ctrl chains, DBPs and ctrl chains and finally between RBPs and DBPs. The obtained results demonstrated that our selected classification algorithms can further improve predictions constructed with only 5 electrostatic features obtained from low-resolution protein structures. Then, these features show the capability for nucleic acid-binding function prediction and also for discriminating RNA-binding from DNA-binding function. Adding more informative features to the prediction process can improve the performance measures and hence increase the classification accuracy.
Two robust classifiers in 3 different comparisons were RBFClassifer and MLPClssifier. These two classifiers have architectures similar to neural network. Therefore, similar to Ahmad and Sarai work 53, neural network methods show significant robustness for prediction of nucleic acid-binding function with the help of electrostatic features.
The importance of this work is to support this idea that simple electrostatic features such as charge, dipole and quadruple moments are useful for identification of nucleic acid-binding function. These electrostatic features can be combined with other sequence or structure-based features for precise function prediction at the level of nucleic acid-binding. Also, the discrimination between RNA- and DNA-binding function is reinforced by these features as well as other suitable ones.
Given the relatively small sizes of the nucleic acid-binding proteins analyzed in this study, discrepancies in the results obtained using different classifiers to predict nucleic acid-binding proteins must be interpreted with caution. It will be important to evaluate these methods on larger, more complete datasets of experimentally validated nucleic acid-binding proteins as they become available.
Notes for practical implementation: The overall workflow for practical implementation of our method is shown in figure 4. Given the protein structure as the input, five electrostatic features are extracted from the query protein. Then, the RBFClassifier as the most powerful method in discriminating between RNA/DNA-binding and non-nucleic acid-binding proteins uses its trained structure to determine whether the query is a RNA/DNA-binding protein or not. If the protein function was predicted as nucleic acid-binding (or RNA/DNA-binding), then the classification continues to distinguish RNA-binding function from DNA-binding one utilizing the trained structure of MLPClassifier, i.e. the best-selected classifier trained on the nucleic acid-binding proteins dataset.

 

Conclusion :

Nine different machine-learning algorithms other than neural network with a higher capability for nucleic acid-binding function prediction have been introduced. These classifiers showed reasonable improvement that highlights their potential to be used by other researchers for nucleic acid–binding function prediction. It is hoped that the use of alternative sequence or structure-based features as well as the electrostatic features will reinforce nucleic acid-binding function prediction protocols.

 

Acknowledgement :

We would like to thank Dr Ebrahim Barzegari Asadabadi for his useful comments.

 



<p>Figure 1. ROC curves of nine machine-learning algorithms employ-ed on RNA-binding protein chains versus ctrl protein chains dataset (consisting of 2601 protein chains) using the LOOCV test. Abbreviations: ADTree, Alternating Decision Tree; K-NN, K-Nearest Ne-ighbor;&nbsp; L1 RLR, L1 Regularized Logistic Regression; L2 RLR, L2 Regularized Logistic Regression; MLPClassifier, Multilayer Per-ceptron Classifier; RBFClassifier, Radial Basis Function Classifier; SMO, Sequential Minimal Optimization; LOOCV, Leave-One-Out Cross-Validation.</p>

Figure 1. ROC curves of nine machine-learning algorithms employ-ed on RNA-binding protein chains versus ctrl protein chains dataset (consisting of 2601 protein chains) using the LOOCV test. Abbreviations: ADTree, Alternating Decision Tree; K-NN, K-Nearest Ne-ighbor;  L1 RLR, L1 Regularized Logistic Regression; L2 RLR, L2 Regularized Logistic Regression; MLPClassifier, Multilayer Per-ceptron Classifier; RBFClassifier, Radial Basis Function Classifier; SMO, Sequential Minimal Optimization; LOOCV, Leave-One-Out Cross-Validation.


<p>Figure 2. ROC curves of nine machine-learning algorithms employed on DNA-binding protein chains versus ctrl protein chains dataset (consisting of 2584 protein chains) using the LOOCV test. Abbreviations: ADTree, Alternating Decision Tree;&nbsp; K-NN, K-Near-est Neighbor; L1 RLR, L1 Regularized Logistic Regression; L2 RLR, L2 Regularized Logistic Regression; MLPClassifier, Multilayer Perceptron Classifier; RBFClassifier, Radial Basis Function Classifier; SMO, Sequential Minimal Optimization; LOOCV, Leave- One-Out Cross-Validation.</p>

Figure 2. ROC curves of nine machine-learning algorithms employed on DNA-binding protein chains versus ctrl protein chains dataset (consisting of 2584 protein chains) using the LOOCV test. Abbreviations: ADTree, Alternating Decision Tree;  K-NN, K-Near-est Neighbor; L1 RLR, L1 Regularized Logistic Regression; L2 RLR, L2 Regularized Logistic Regression; MLPClassifier, Multilayer Perceptron Classifier; RBFClassifier, Radial Basis Function Classifier; SMO, Sequential Minimal Optimization; LOOCV, Leave- One-Out Cross-Validation.


<p>Figure 3. ROC curves of nine machine-learning algorithms employed on RNA-binding protein chains versus DNA-binding protein chains dataset (consisting of 303 protein chains) using the LOOCV test. Abbreviations: ADTree, Alternating Decision Tree;&nbsp; K-NN, K-Nearest Neighbor; L1 RLR, L1 Regularized Logistic Regression; L2 RLR, L2 Regularized Logistic Regression; MLPClassifier, Multilayer Perceptron Classifier; RBFClassifier, Radial Basis Function Classifier; SMO, Sequential Minimal Optimization; LOOCV, Leave-One-Out Cross-Validation.</p>

Figure 3. ROC curves of nine machine-learning algorithms employed on RNA-binding protein chains versus DNA-binding protein chains dataset (consisting of 303 protein chains) using the LOOCV test. Abbreviations: ADTree, Alternating Decision Tree;  K-NN, K-Nearest Neighbor; L1 RLR, L1 Regularized Logistic Regression; L2 RLR, L2 Regularized Logistic Regression; MLPClassifier, Multilayer Perceptron Classifier; RBFClassifier, Radial Basis Function Classifier; SMO, Sequential Minimal Optimization; LOOCV, Leave-One-Out Cross-Validation.


<p>Figure 4. The overall workflow for practical implementation. Firstly, the query protein is represented numerically by three kinds of features. Secondly, the first round of the classification is done using the best-selected classifier trained on combined full dataset (i.e. the RBFClassifier). Thirdly, if the function of query protein was predicted as nucleic acid-binding (or RNA/DNA-binding), the second round of the classification is attempted based on the best-selected classifier trained on the nucleic acid-binding proteins dataset (<em>i.e</em>. the MLPClassifier). The final predicted function identifies the query protein as either RNA-binding or DNA-binding. Abbreviations: MLPClassifier, Multilayer Perceptron Classifier; RBFClassifier, Radial Basis Function Classifier.</p>

Figure 4. The overall workflow for practical implementation. Firstly, the query protein is represented numerically by three kinds of features. Secondly, the first round of the classification is done using the best-selected classifier trained on combined full dataset (i.e. the RBFClassifier). Thirdly, if the function of query protein was predicted as nucleic acid-binding (or RNA/DNA-binding), the second round of the classification is attempted based on the best-selected classifier trained on the nucleic acid-binding proteins dataset (i.e. the MLPClassifier). The final predicted function identifies the query protein as either RNA-binding or DNA-binding. Abbreviations: MLPClassifier, Multilayer Perceptron Classifier; RBFClassifier, Radial Basis Function Classifier.


<p>Table 1. Performance measures of nine different classification algorithms applied on the RNA-binding protein chains and Ctrl chains, in a LOOCV<sup>k</sup> analysis</p> <p>a: Alternating Decision Tree; b: K- Nearest Neighbor; c: L1 Regularized Logistic Regression; d: L2 Regularized Logistic Regression; e: Multilayer Perceptron Classifier; f: Random Forest; g: Radial Basis Function Classifier; h: Sequential Minimal Optimization; i: Neural Network; j: Area Under the receiver operating characteristic Curve; k: Leave-One-Out Cross-Validation; l: Data obtained from Ahmad and Sarai work <sup>53</sup>.</p>

Table 1. Performance measures of nine different classification algorithms applied on the RNA-binding protein chains and Ctrl chains, in a LOOCVk analysis

a: Alternating Decision Tree; b: K- Nearest Neighbor; c: L1 Regularized Logistic Regression; d: L2 Regularized Logistic Regression; e: Multilayer Perceptron Classifier; f: Random Forest; g: Radial Basis Function Classifier; h: Sequential Minimal Optimization; i: Neural Network; j: Area Under the receiver operating characteristic Curve; k: Leave-One-Out Cross-Validation; l: Data obtained from Ahmad and Sarai work 53.


<p>Table 2. Performance measures of nine different classification algorithms applied on the DNA-binding protein chains and Ctrl chains, in a LOOCV<sup>k</sup> procedure</p> <p>a: Alternating Decision Tree; b: K- Nearest Neighbor; c: L1 Regularized Logistic Regression; d: L2 Regularized Logistic Regression; e: Multilayer Perceptron Classifier; f: Random Forest; g: Radial Basis Function Classifier; h: Sequential Minimal Optimization; i: Neural Network; j: Area Under the receiver operating characteristic Curve; k: Leave-One-Out Cross-Validation; l: Data obtained from Ahmad and Sarai work <sup>53</sup>.</p>

Table 2. Performance measures of nine different classification algorithms applied on the DNA-binding protein chains and Ctrl chains, in a LOOCVk procedure

a: Alternating Decision Tree; b: K- Nearest Neighbor; c: L1 Regularized Logistic Regression; d: L2 Regularized Logistic Regression; e: Multilayer Perceptron Classifier; f: Random Forest; g: Radial Basis Function Classifier; h: Sequential Minimal Optimization; i: Neural Network; j: Area Under the receiver operating characteristic Curve; k: Leave-One-Out Cross-Validation; l: Data obtained from Ahmad and Sarai work 53.


<p>Table 3. Performance measures of nine different classification algorithms applied on the RNA-binding protein chains and DNA-binding protein chains, in a LOOCV<sup>k</sup> process</p> <p>a: Alternating Decision Tree; b: K- Nearest Neighbor; c: L1 Regularized Logistic Regression; d: L2 Regularized Logistic Regression; e: Multilayer Perceptron Classifier; f: Random Forest; g: Radial Basis Function Classifier; h: Sequential Minimal Optimization; i: Neural Network; j: Area Under the receiver operating characteristic Curve; k: Leave-One-Out Cross-Validation; l: Data obtained from Ahmad and Sarai work <sup>53</sup>.</p>

Table 3. Performance measures of nine different classification algorithms applied on the RNA-binding protein chains and DNA-binding protein chains, in a LOOCVk process

a: Alternating Decision Tree; b: K- Nearest Neighbor; c: L1 Regularized Logistic Regression; d: L2 Regularized Logistic Regression; e: Multilayer Perceptron Classifier; f: Random Forest; g: Radial Basis Function Classifier; h: Sequential Minimal Optimization; i: Neural Network; j: Area Under the receiver operating characteristic Curve; k: Leave-One-Out Cross-Validation; l: Data obtained from Ahmad and Sarai work 53.


References :
  1. Castello A, Fischer B, Frese CK, Horos R, Alleaume AM, Foehr S, et al. Comprehensive identification of RNA-binding domains in human cells. Mol Cell 2016;63(4):696-710.   [PubMed]
  2. Kloetgen A, Munch PC, Borkhardt A, Hoell JI, McHardy AC. Biochemical and bioinformatic methods for elucidating the role of RNA-protein interactions in posttranscriptional regulation. Brief Funct Genomics 2014;14(2):102-114.   [PubMed]
  3. Lunde BM, Moore C, Varani G. RNA-binding proteins: modular design for efficient function. Nat Rev Mol Cell Biol 2007;8(6):479-490.   [PubMed]
  4. Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 2003;31(13):3692-3697.   [PubMed]
  5. Cai YD, Lin SL. Support vector machines for predicting rRNA-, RNA-, and DNA- binding proteins from amino acid sequence. Biochim Biophys Acta 2003;1648(1-2):127-133.   [PubMed]
  6. Han LY, Cai CZ, Lo SL, Chung MC, Chen YZ. Prediction of RNA-binding proteins from primary sequence by a support vector machine approach. RNA 2004;10(3):355-368.   [PubMed]
  7. Yu X, Cao J, Cai Y, Shi T, Li Y. Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines. J Theor Biol 2006;240(2):175-184.   [PubMed]
  8. Shao X, Tian Y, Wu L, Wang Y, Jing L, Deng N. Predicting DNA- and RNA- binding proteins from sequences with kernel methods. J Theor Biol 2009;258(2):289-293.   [PubMed]
  9. Spriggs RV, Murakami Y, Nakamura H, Jones S. Protein function annotation from sequence: prediction of residues interacting with RNA. Bioinformatics 2009;25 (12):1492-1497.   [PubMed]
  10. Kumar M, Gromiha MM, Raghava GPS. SVM based prediction of RNA-binding proteins using binding residues and evolutionary information. J Mol Recognit 2011;24(2):303-313.   [PubMed]
  11. Peng CR, Liu L, Niu B, Lv YL, Li MJ, Yuan YL, et al. Prediction of RNA-binding proteins by voting systems. J Biomed Biotechnol 2011;2011:506205.   [PubMed]
  12. Agostini F, Zanzoni A, Klus P, Marchese D, Cirillo D, Targatlia GG. catRAPID omics: a web server for large-scale prediction of protein-RNA interactions. Bioinformatics 2013;29(22):2928-2930.   [PubMed]
  13. Klus P, Bolognesi B, Agostini F, Marchese D, Zanzoni A, Targatlia GG. The cleverSuite approach for protein characterization: predictions of structural properties, solubility, chaperon requirements and RNA-binding abilities. Bioinformatics 2014;30(11):1601-1608.   [PubMed]
  14. Livi CM, Klus P, Delli Ponti R, Targatlia GG. catRAPID signature: identification of ribonucleoproteins and RNA-binding regions. Bioinformatics 2016;32(5):773-775.   [PubMed]
  15. Ma X, Guo J, Sun X. Sequence-based prediction of RNA-binding proteins using random forest with minimum redundancy maximum relevance feature selection. Biomed Res Int 2015;2015:425810.   [PubMed]
  16. Zhang X, Liu S. RBPPred: predicting RNA-binding proteins from sequence using SVM. Bioinformatics 2017;33(6):854-862.   [PubMed]
  17. Sharan M, Forstner KU, Eulalio A, Vogel J. APRICOT: an integrated computational pipeline for the sequence-based identification and characterization of RNA-binding proteins. Nucleic Acids Res 2017;45(11):e96.   [PubMed]
  18. Shazman S, Mandel-Gutfreund Y. Classifying RNA-binding proteins based on electrostatic properties. PLoS Comput Biol 2008;4(8):e1000146.   [PubMed]
  19. Paz I, Kligun E, Bengud B, Mandel-Gutfreund Y. BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins. Nucleic Acids Res 2016;44(W1):W568-574.   [PubMed]
  20. Zhao H, Yang Y, Zhou Y. Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets. Nucleic Acids Res 2011;39(8):3017-3025.   [PubMed]
  21. Zhao H, Yang Y, Zhou Y. Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction. RNA Biol 2011;8(6):988-996.   [PubMed]
  22. Yang Y, Zhan J, Zhao H, Zhou Y. A new size-independent score for pairwise protein structure alignment and its application to structure classification and nucleic-acid binding prediction. Proteins 2012;80(8):2080-2088.   [PubMed]
  23. Yang Y, Zhao H, Wang J, Zhou Y. SPOT-Seq-RNA: predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction. Methods Mol Biol 2014;1137:119-130.   [PubMed]
  24. Zhao H, Yang Y, Janga SC, Kao CC, Zhou Y. Prediction and validation of the unexplored RNA-binding protein atlas of the human proteome. Proteins 2014;82(4):640-647.   [PubMed]
  25. Puton T, Kozlowski L, Tuszynska I, Rother K, Bujnicki JM. Computational methods for prediction of protein-RNA interactions. J Struct Biol 2012;179(3):261-268.   [PubMed]
  26. Zhao H, Yang Y, Zhou Y. Prediction of RNA binding proteins comes of age from low resolution to high resolution. Mol BioSyst 2013;9(10):2417-2425.   [PubMed]
  27. Si J, Cui J, Cheng J, Wu R. Computational prediction of RNA-binding proteins and binding sites. Int J Mol Sci 2015;16(11):26303-26317.   [PubMed]
  28. Jones S, Barker JA, Nobeli I, Thornton JM. Using structural motif templates to identify proteins with DNA binding function. Nucleic Acids Res 2003;31(11):2811-2823.   [PubMed]
  29. Ahmad S, Sarai A. Moment-based prediction of DNA-binding proteins. J Mol Biol 2004;341(1):65-71.   [PubMed]
  30. Shanahan HP, Garcia MA, Jones S, Thornton JM. Identifying DNA-binding proteins using structural motifs and the electrostatic potential. Nucleic Acids Res 2004;32(16):4732-4741.   [PubMed]
  31. Bhardwaj N, Langolis RE, Zhao G, Lu H. Kernel-based machine learning protocol for predicting DNA-binding proteins. Nucleic Acids Res 2005;33(20):6486-6493.   [PubMed]
  32. Gao M, Skolnick J. A threading-based method for the prediction of DNA-binding proteins with application to the human genome. PLoS Comput Biol 2009;5(11):e1000567.   [PubMed]
  33. Nimrod G, Szilagyi A, Leslie C, Ben-tal N. Identification of DNA-binding proteins using structural, electrostatic and evolutionary features. J Mol Biol 2009;387(4):1040-1053.   [PubMed]
  34. Nimrod G, Schushan M, Szilagyi A, Leslie C, Ben-Tal N. iDBPs: a web server for the identification of DNA binding proteins. Bioinformatics 2010;26(5):692-693.   [PubMed]
  35. Zhao H, Yang Y, Zhou Y. Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function. Bioinformatics 2010;26(15):1857-1863.   [PubMed]
  36. Zhao H, Wang J, Zhou Y, Yang Y. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome. PLoS One 2014;9(5):e96694.   [PubMed]
  37. Kumar M, Gromiha MM, Raghava GPS. Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics 2007;8:463.   [PubMed]
  38. Fang Y, Guo Y, Feng Y, Li M. Predicting DNA-binding proteins: approached from Chou s pseudo amino acid composition and other specific sequence features. Amino Acids 2008;34(1):103-109.   [PubMed]
  39. Nani L, Lumini A. Combining ontologies and dipeptide composition for predicting DNA-binding proteins. Amino Acids 2008;34(4):635-641.   [PubMed]
  40. Kumar KK, Pugalenthi G, Suganthan PN. DNA-Prot: Identification of DNA binding proteins from protein sequence information using random forest. J Biomol Struct Dyn 2009;26(6):679-686.   [PubMed]
  41. Nani L, Lumini A. An ensemble of reduced alphabets with protein encoding based on grouped weight for predicting DNA-binding proteins. Amino Acids 2009;36 (2):167-175.   [PubMed]
  42. Lin WZ, Fang JA, Xiao X, Chou KC. iDNA-Prot: identification of DNA binding proteins using random forest with grey model. PLoS One 2011;6(9):e24756.   [PubMed]
  43. Zou C, Gong J, Li H. An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis. BMC Bioinformatics 2013;14:90.   [PubMed]
  44. Song L, Li D, Zeng X, Wu Y, Guo L, Zou Q. nDNA-prot: identification of DNA-binding proteins based on unbalanced classification. BMC Bioinformatics 2014;15:298.   [PubMed]
  45. Xu R, Zhou J, Liu B, Yao L, He Y, Zou Q, et al. enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning. Biomed Res Int 2014;2014:294279.   [PubMed]
  46. Liu B, Xu J, Lan X, Xu R, Zhou J, Wang X, et al. iDNA-Protdis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. PLoS One 2014;9(9):e106691.   [PubMed]
  47. Lou W, Wang X, Chen F, Chen Y, Jiang B, Zhang H. Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian Naïve Bayes. PLoS One 2014;9(1):e86703.   [PubMed]
  48. Niu XH, Hu XH, Shi F, Xia JB. Predicting DNA binding proteins using support vector machine with hybrid fractal features. J Theor Biol 2014;343:186-192.   [PubMed]
  49. Zhang Y, Xu J, Zheng W, Zhang C, Qiu X, Chen K, et al. newDNA-Prot: Prediction of DNA-binding proteins by employing support vector machine and a comprehensive sequence representation. Comput Biol Chem 2014;52:51-59.   [PubMed]
  50. Liu B, Xu J, Fan S, Xu R, Zhou J, Wang X. PseDNA-Pro: DNA-binding protein identification by combining Chou’s PseAAC and physicochemical distance transformation. Mol Inform 2015;34(1):8-17.   [PubMed]
  51. Xu R, Zhou J, Wang H, He Y, Wang X, Liu B. Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation. BMC Syst Biol 2015;9 Suppl 1:S10.   [PubMed]
  52. Zhang J, Gao B, Chai H, Ma Z, Yang G. Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm. BMC Bioinformatics 2016;17(1):323.   [PubMed]
  53. Ahmad S, Sarai A. Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction. BMC Struct Biol 2011;11:8.   [PubMed]
  54. Kukic P, Nielsen JE. Electrostatics in proteins and protein-ligand complexes. Future Med Chem 2010;2(4):647-666.   [PubMed]
  55. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: An update. SIGKDD Explor 2009;11(1):10-18.
  56. Demir-Kavuk O, Kamada M, Akutsu T, Knapp EW. Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features. BMC Bioinformatics 2011;12:412.   [PubMed]
  57. Asadabadi EB, Abdolmaleki P. Predictions of protein-protein interfaces within membrane protein complexes. Avicenna J Med Biotechnol 2013;5(3):148-157.   [PubMed]
  58. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics 2005;21(20):3940-3941.   [PubMed]
  59. R-package Development Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing 2013, Vienna, Austria. URL http://www.R-project.org/.