BACKGROUND
in this post-genomic era, protein, as the major component of organism, is widely studied because of its important role in nearly all cell functions including dna transcription and replication, metabolic cycles, and signaling cascades.
researches show that many functions of complex biological systems seem to be more closely determined by their interactions rather than their individual components.
therefore, the protein-protein interaction networks have been dawning increasing research attentions and interests.
moreover, the recent advance in practical applications in drug discovery comes to be a promotion factor for studies on ppis which provides great insights into mechanisms of human diseases.
efforts have been devoted to the development of experimental methods for detecting ppis and constructing protein interaction networks, such as yeast two-hybrid   <cit>  screens, tandem affinity purification   <cit> , mass spectrometric protein complex identification   <cit>  and other high-throughput biological techniques for ppis detection.
however, experimental methods are expensive, time-consuming and tedious.
meanwhile experimentally identified ppis are usually associated with high rates of both false positive and false negative results.
for the sake of detecting larger fraction of the whole ppi network and utilizing the valuable and vast biological data provided by experimental methods, there is a growing need to develop computational methods capable of identifying ppis.
a number of computational approaches haven been proposed for detecting ppis based on various data types, such as genomic information, protein domain and protein structure information  <cit> .
however, these methods are limited by the need of prior information about proteins, and the accuracies of them are sensitive to the reliability of the prior information.
in addition, the exponential growth of newly discovered protein sequences is accumulated in numerous different types of databases.
therefore, it is significant to develop sequence-based ppi predicting systems mining information directly from amino acid sequences.
many researchers have engaged in trials to establish sequence-based system for predicting ppis and have gained some preliminary result.
to solve this problem, zhou et al.
<cit>  proposed an approach combing support vector machine and local protein sequence descriptors which account for the interactions between sequentially distant amino acid residues.
when applied to predicting yeast ppis, this method yielded a promising accuracy of  <dig> %.
najafabadi et al.
<cit>  found similarity in codon usage is a strong predictor for expressing proteins and got a 75% increase in sensitivity in his experience considering codon usage.
shi et al.
<cit>  explored a kind of descriptor named correlation coefficient transformation and used support vector machine and this method adequately considers the neighboring effect and the level of correlation coefficient.
computational systems for predicting pairwise protein interactions usually rely on two main components: feature extraction and machine learning model.
efficient feature descriptors are capable of mining useful information and normalizing different-length proteins to the same size.
furthermore, effective feature extraction methods can lead to an improvement in prediction performance.
until now, a number of feature extraction approaches based on protein sequence have been proposed and most of them consider the sequence order effect.
in fact, employing graphic approaches to mine proteins’ information would be of great novelty.
in this work, we adopt a novel descriptor named cw-lbp and show it is sufficient to reveal the complicated relations between protein interactions and their amino acid sequences.
this sequence representation first encodes the protein sequence as a numerical sequence by substituting each amino acid with a specific proteins’ physicochemical property.
then, meyer continuous wavelet transformation is employed to represent a protein sequence as an image.
finally, an image texture descriptor, local binary pattern histogram fourier  is used to extract features.
in order to describe a protein in a discrete model which could provide comprehensive information, chou’s pseudo amino acid composition  is employed as another kind of feature descriptor.
pseaac is a popular protein descriptor using the first  <dig> factors to reflect components of  <dig> conventional amino acid  compositions and λ additional factors to reflect the influence of sequence order.
as the second step of computational methods for predicting ppis, a wide range of machine learning models have been applied in previous works.
however, the popular classifiers such as svm  <cit>  and neural network  <cit>  need much effort to adjust the optimal parameters.
recently, sparse representation based classification  comes to be a new technique in study of face recognition for its excellent ability against illumination variations, occlusions, and random noise.
matching the feature descriptors extracted by the proposed graphic-based features , src would be an ideal classification model.
as indicated in the study of  <cit> , weighted sparse representation based classifier , a variant of basic src, additionally consider the local information of each training samples and therefore have a strong classification ability surpassing original src.
in addition, wsrc needs little manual invention to adjust the optimal parameters, which is a significant character for the vast data volume of various protein sequence sets.
thus, wsrc algorithm is used as the machine learning tool to make the final prediction based on the extracted feature sets.
in this study, we report a novel computational method for predicting protein-protein interactions based on amino acid sequences by using the classifier of wsrc and the combined features consisting of cw-lbp and pseaac descriptors.
firstly, each protein is transformed into a cw image deriving from amino acid sequence and then cw-lbp features are extracted from these images using lbp-hf texture descriptor.
secondly, for a more comprehensive representation for protein sequences, we extracted the chou’s pseudo amino acid composition of each sample and merged it with cw-lbp descriptor as the whole feature set.
by doing this, our feature representation of one protein would own  <dig> dimensions of which  <dig> come from cw-lbp descriptor and  <dig> is the chou’s pseaa composition.
finally, wsrc is utilized to deal with the classification.
to evaluate the performance, the proposed approach is applied to three different ppi data sets: saccharomyces cerevisiae, human, and h.pylori.
RESULTS
evaluation measures
to evaluate the performance of the proposed method, we use five-fold cross validation and a couple of evaluation measures such as the overall prediction accuracy , sensitivity , precision  and matthews correlation coefficient  in this study.
these criteria are defined as follows:  <dig> accuracy=tp+tntp+fp+tn+fn
<dig> sensitivity=tptp+fn
<dig> pe=tptp+fp
<dig> mcc=tp×tn−fp×fntp+fn×tn+fp×tp+fp×tn+fn where true positive  denotes the number of true samples which are predicted correctly; false negative  is the number of true samples predicted to be non-interacting pairs incorrectly; false positive  is the number of true non-interacting pairs predicted to be ppis falsely, and true negative  is the number of true non-interacting pairs predicted correctly.
furthermore, the receiver operating characteristic  curve was also used to evaluate the performance of proposed method.
summarizing roc curve in a numerical way, the area under an roc curve  was computed.
a higher auc value means a better result performed.
assessment of prediction ability
for the sake of impartiality, we set the same corresponding parameters  for wsrc when we explored using the proposed method to predict ppis of saccharomyces cerevisiae and h.plpori dataset.
in order to minimize the overfitting of the prediction model and test the robustness of the proposed method, 5-fold cross-validation was used in our experiments.
in 5-fold cross-validation, dataset would be divided into five parts which four of them are used for training and the rest one of them is used for testing.
by this way, five models were generated from the original dataset.
the prediction results of src prediction models with continuous wavelet features and pseaa composition are shown in table  <dig> and table  <dig>  for all five models of saccharomyces cerevisiae dataset, the prediction accuracies are ≥  <dig> %, the precisions are ≥  <dig> %, and the sensitivities are  <dig> %.
for the five models of h.pylori dataset, the prediction accuracies are ≥  <dig> %, the precisions are ≥  <dig> % and the sensitivities are ≥  <dig> %.
in order to better evaluate the practical prediction ability of the proposed model, we also calculate the mcc and auc values .
from table  <dig> and table  <dig>  it can be observed that the averages of mcc and auc score of saccharomyces cerevisiae dataset are  <dig> % and  <dig> % respectively.table  <dig> 5-fold cross validation result obtained in predicting yeast ppis dataset
fig.
<dig> the flowchart for the feature extraction process
fig.
<dig> roc curves from proposed method result for saccharomyces cerevisiae ppis dataset
when predicting the ppis of h.pylori dataset, the averages of mcc and auc come to be  <dig> % and  <dig> % .
further, we can see that our method achieved a stable performance with the low standard deviations of accuracy, precision, sensitivity, mcc and auc as  <dig> %,  <dig> %, and  <dig> %,  <dig> % and  <dig> % respectively.fig.
<dig> roc curves from proposed method result for h.pylori ppis dataset
comparison with svm-based method
many machine learning models haven been proposed for predicting the protein-protein interactions and one of the most popular classifiers is support vector machine .
to further evaluate the proposed method, using the same feature extraction method, we explored svm for predicting ppis of human dataset to compare with the performance of wsrc.
here, two parameters c and g of svm were optimized by using a grid search method.
parameters c and g were set to be  <dig> and  <dig> respectively.
we also used 5-fold cross-validation in these experiments.
the results performed by wsrc and svm are shown in table  <dig> table  <dig> 5-fold cross validation result obtained in predicting human ppis dataset
it can be observed that wsrc yielded good results with averages of accuracy, precision, sensitivity and mcc as high as  <dig> %,  <dig> %,  <dig> % and  <dig> % respectively.
when using svm for the prediction, the averages of accuracy, precision, sensitivity and mcc come to be  <dig> %,  <dig> %,  <dig> % and  <dig> % respectively, lower than the results from the wsrc-based model.
from the roc curves of fig.
<dig> and fig.
<dig>  we can see that the average auc score of wsrc model was  <dig> %, higher than that of svm model.
in addition, it can be noticed that the standard deviations of accuracy, precision, sensitivity, mcc yielded by wsrc model are as low as  <dig> %,  <dig> %,  <dig> % and  <dig> %, lower than those of svm model which are  <dig> %,  <dig> %,  <dig> % and  <dig> % respectively.
analyzing all these results, we consider the proposed method based on wsrc is superior to the svm-based method.fig.
<dig> roc curves from proposed method result for human ppis dataset
fig.
<dig> roc curves from svm-based method result for human ppis dataset
comparison with other methods
many methods have been proposed for predicting ppis.
here, we compare the prediction ability of the src prediction model using continuous wavelet transform descriptors and psaaa composition with the existing methods.
from table  <dig>  we can see the results of 5-fold cross-validation from different methods on the saccharomyces cerevisiae dataset.
most averages of accuracy, precision and sensitivity yielded by other methods are lower than the results yielded by the proposed method.
meanwhile, we can see that the proposed method is more stable than other methods with relatively low standard deviations of accuracy, precision, sensitivity and mcc as  <dig> %,  <dig> %,  <dig> % and  <dig> % respectively.
the average results performed by other methods on h.pylori dataset are shown in table  <dig> table  <dig> performance comparison of different methods on the yeast dataset
discussion
in the proposed model, the protein features are extracted by using the transformations of numerical sequences, continuous wavelet and local binary pattern histogram fourier.
this feature extraction method is mainly based on the assumptions that the information of protein sequences can provide enough information for predicting protein-protein interactions and the fact that the hydrophobicity character of protein influences the protein interacting process.
to retain comprehensive information by feature extraction, there are two kinds of descriptors, namely cw-lbp and pseaac, adopted to capture the continuous and discrete information, respectively.
in addition, in order to combine with the cw-lbp feature well and to develop a prediction model which need little manual intervention, the classification method of weighted sparse representation-based classifier is used to make the final prediction.
it is worthwhile to highlight several aspects of the proposed approach based on the experiments results here.
the outstanding prediction performance shows that continuous wavelet transformation can cooperate well with the local binary pattern histogram fourier for protein feature extraction.
the comparison result of wsrc versus svm demonstrates that wsrc can be well combined with graph-based feature extraction method and the use of cw-lbp may help wsrc give a full play to its function.
it is worth noting that wsrc could yield stable and satisfactory prediction performance by keeping the same parameters in all experiment.
compared with other conventional classifiers including svm, wsrc has a valuable advantage that it doesn’t need much manual intervention to adjust the optimal parameters and therefore has great potential to be applied to the large-scale prediction for new ppis.
it is known that approaches using ensemble classifier usually achieve more accurate and robust performance than the methods using single classifier.
however, using the single classifier, our proposed model obtains good performance similar to those obtained by the methods using ensemble classifier such as boosting.
from these comparisons, it is demonstrated that the wsrc-based model combining the continuous wavelet transform descriptor and pseaa composition can improve the prediction accuracy compared with current state-of-the-art classification mothods.
CONCLUSIONS
the growing demand for ppis knowledge is promoting the development of studies on computational methods for predicting ppis.
in this paper, we propose a new ppis prediction model only using the information of protein sequences.
since hydrophilic interaction plays an important role in the process of protein interactions, we consider the hydrophobic property of amino acids in the process of feature extraction by transforming protein sequences into numerical ones.
we then adopted continuous wavelet descriptors and chou’s pseudo amino acid composition, which aims at capturing the continuous and discrete information from the hydrophobic sequences.
besides, weighted sparse representation based classifier was used as the sample classification model due to its advantages of low manual intervention in parameter adjustion and good cooperation with features.
results obtained from our experiments have shown that it is a good attempt to represent proteins using graphic texture extraction approaches.
our proposed method is feasible and effective.
when performed on the saccharomyces cerevisiae, human and h.pylori datasets, the proposed method achieved promising results with high average accuracies of  <dig> %,  <dig> % and  <dig> % respectively.

 in this post-genomic era, protein, as the major component of organism, is widely studied because of its important role in nearly all cell functions including dna transcription and replication, metabolic cycles, and signaling cascades. researches show that many functions of complex biological systems seem to be more closely determined by their interactions rather than their individual components. therefore, the protein-protein interaction networks have been dawning increasing research attentions and interests. moreover, the recent advance in practical applications in drug discovery comes to be a promotion factor for studies on ppis which provides great insights into mechanisms of human diseases. efforts have been devoted to the development of experimental methods for detecting ppis and constructing protein interaction networks, such as yeast two-hybrid screens, tandem affinity purification, mass spectrometric protein complex identification and other high-throughput biological techniques for ppis detection. however, experimental methods are expensive, time-consuming and tedious. meanwhile experimentally identified ppis are usually associated with high rates of both false positive and false negative results. for the sake of detecting larger fraction of the whole ppi network and utilizing the valuable and vast biological data provided by experimental methods, there is a growing need to develop computational methods capable of identifying ppis. a number of computational approaches haven been proposed for detecting ppis based on various data types, such as genomic information, protein domain and protein structure information. however, these methods are limited by the need of prior information about proteins, and the accuracies of them are sensitive to the reliability of the prior information. in addition, the exponential growth of newly discovered protein sequences is accumulated in numerous different types of databases. therefore, it is significant to develop sequence-based ppi predicting systems mining information directly from amino acid sequences. many researchers have engaged in trials to establish sequence-based system for predicting ppis and have gained some preliminary result. to solve this problem, zhou et al. proposed an approach combing support vector machine and local protein sequence descriptors which account for the interactions between sequentially distant amino acid residues. when applied to predicting yeast ppis, this method yielded a promising accuracy of %. najafabadi et al. found similarity in codon usage is a strong predictor for expressing proteins and got a 75% increase in sensitivity in his experience considering codon usage. shi et al. explored a kind of descriptor named correlation coefficient transformation and used support vector machine and this method adequately considers the neighboring effect and the level of correlation coefficient. computational systems for predicting pairwise protein interactions usually rely on two main components: feature extraction and machine learning model. efficient feature descriptors are capable of mining useful information and normalizing different-length proteins to the same size. furthermore, effective feature extraction methods can lead to an improvement in prediction performance. until now, a number of feature extraction approaches based on protein sequence have been proposed and most of them consider the sequence order effect. in fact, employing graphic approaches to mine proteins’ information would be of great novelty. in this work, we adopt a novel descriptor named cw-lbp and show it is sufficient to reveal the complicated relations between protein interactions and their amino acid sequences. this sequence representation first encodes the protein sequence as a numerical sequence by substituting each amino acid with a specific proteins’ physicochemical property. then, meyer continuous wavelet transformation is employed to represent a protein sequence as an image. finally, an image texture descriptor, local binary pattern histogram fourier is used to extract features. in order to describe a protein in a discrete model which could provide comprehensive information, chou’s pseudo amino acid composition is employed as another kind of feature descriptor. pseaac is a popular protein descriptor using the first factors to reflect components of conventional amino acid compositions and λ additional factors to reflect the influence of sequence order. as the second step of computational methods for predicting ppis, a wide range of machine learning models have been applied in previous works. however, the popular classifiers such as svm and neural network need much effort to adjust the optimal parameters. recently, sparse representation based classification comes to be a new technique in study of face recognition for its excellent ability against illumination variations, occlusions, and random noise. matching the feature descriptors extracted by the proposed graphic-based features, src would be an ideal classification model. as indicated in the study of, weighted sparse representation based classifier, a variant of basic src, additionally consider the local information of each training samples and therefore have a strong classification ability surpassing original src. in addition, wsrc needs little manual invention to adjust the optimal parameters, which is a significant character for the vast data volume of various protein sequence sets. thus, wsrc algorithm is used as the machine learning tool to make the final prediction based on the extracted feature sets. in this study, we report a novel computational method for predicting protein-protein interactions based on amino acid sequences by using the classifier of wsrc and the combined features consisting of cw-lbp and pseaac descriptors. firstly, each protein is transformed into a cw image deriving from amino acid sequence and then cw-lbp features are extracted from these images using lbp-hf texture descriptor. secondly, for a more comprehensive representation for protein sequences, we extracted the chou’s pseudo amino acid composition of each sample and merged it with cw-lbp descriptor as the whole feature set. by doing this, our feature representation of one protein would own dimensions of which come from cw-lbp descriptor and is the chou’s pseaa composition. finally, wsrc is utilized to deal with the classification. to evaluate the performance, the proposed approach is applied to three different ppi data sets: saccharomyces cerevisiae, human, and h.pylori.  evaluation measures to evaluate the performance of the proposed method, we use five-fold cross validation and a couple of evaluation measures such as the overall prediction accuracy, sensitivity, precision and matthews correlation coefficient in this study. these criteria are defined as follows: accuracy=tp+tntp+fp+tn+fn sensitivity=tptp+fn pe=tptp+fp mcc=tp×tn−fp×fntp+fn×tn+fp×tp+fp×tn+fn where true positive denotes the number of true samples which are predicted correctly; false negative is the number of true samples predicted to be non-interacting pairs incorrectly; false positive is the number of true non-interacting pairs predicted to be ppis falsely, and true negative is the number of true non-interacting pairs predicted correctly. furthermore, the receiver operating characteristic curve was also used to evaluate the performance of proposed method. summarizing roc curve in a numerical way, the area under an roc curve was computed. a higher auc value means a better result performed. assessment of prediction ability for the sake of impartiality, we set the same corresponding parameters for wsrc when we explored using the proposed method to predict ppis of saccharomyces cerevisiae and h.plpori dataset. in order to minimize the overfitting of the prediction model and test the robustness of the proposed method, 5-fold cross-validation was used in our experiments. in 5-fold cross-validation, dataset would be divided into five parts which four of them are used for training and the rest one of them is used for testing. by this way, five models were generated from the original dataset. the prediction results of src prediction models with continuous wavelet features and pseaa composition are shown in table  and table  for all five models of saccharomyces cerevisiae dataset, the prediction accuracies are ≥  %, the precisions are ≥  %, and the sensitivities are %. for the five models of h.pylori dataset, the prediction accuracies are ≥  %, the precisions are ≥  % and the sensitivities are ≥  %. in order to better evaluate the practical prediction ability of the proposed model, we also calculate the mcc and auc values. from table  and table  it can be observed that the averages of mcc and auc score of saccharomyces cerevisiae dataset are % and % respectively.table 5-fold cross validation result obtained in predicting yeast ppis dataset fig. the flowchart for the feature extraction process fig. roc curves from proposed method result for saccharomyces cerevisiae ppis dataset when predicting the ppis of h.pylori dataset, the averages of mcc and auc come to be % and %. further, we can see that our method achieved a stable performance with the low standard deviations of accuracy, precision, sensitivity, mcc and auc as %, %, and %, % and % respectively.fig. roc curves from proposed method result for h.pylori ppis dataset comparison with svm-based method many machine learning models haven been proposed for predicting the protein-protein interactions and one of the most popular classifiers is support vector machine. to further evaluate the proposed method, using the same feature extraction method, we explored svm for predicting ppis of human dataset to compare with the performance of wsrc. here, two parameters c and g of svm were optimized by using a grid search method. parameters c and g were set to be and respectively. we also used 5-fold cross-validation in these experiments. the results performed by wsrc and svm are shown in table  table 5-fold cross validation result obtained in predicting human ppis dataset it can be observed that wsrc yielded good results with averages of accuracy, precision, sensitivity and mcc as high as %, %, % and % respectively. when using svm for the prediction, the averages of accuracy, precision, sensitivity and mcc come to be %, %, % and % respectively, lower than the results from the wsrc-based model. from the roc curves of fig.  and fig.  we can see that the average auc score of wsrc model was %, higher than that of svm model. in addition, it can be noticed that the standard deviations of accuracy, precision, sensitivity, mcc yielded by wsrc model are as low as %, %, % and %, lower than those of svm model which are %, %, % and % respectively. analyzing all these results, we consider the proposed method based on wsrc is superior to the svm-based method.fig. roc curves from proposed method result for human ppis dataset fig. roc curves from svm-based method result for human ppis dataset comparison with other methods many methods have been proposed for predicting ppis. here, we compare the prediction ability of the src prediction model using continuous wavelet transform descriptors and psaaa composition with the existing methods. from table  we can see the results of 5-fold cross-validation from different methods on the saccharomyces cerevisiae dataset. most averages of accuracy, precision and sensitivity yielded by other methods are lower than the results yielded by the proposed method. meanwhile, we can see that the proposed method is more stable than other methods with relatively low standard deviations of accuracy, precision, sensitivity and mcc as %, %, % and % respectively. the average results performed by other methods on h.pylori dataset are shown in table  table performance comparison of different methods on the yeast dataset discussion in the proposed model, the protein features are extracted by using the transformations of numerical sequences, continuous wavelet and local binary pattern histogram fourier. this feature extraction method is mainly based on the assumptions that the information of protein sequences can provide enough information for predicting protein-protein interactions and the fact that the hydrophobicity character of protein influences the protein interacting process. to retain comprehensive information by feature extraction, there are two kinds of descriptors, namely cw-lbp and pseaac, adopted to capture the continuous and discrete information, respectively. in addition, in order to combine with the cw-lbp feature well and to develop a prediction model which need little manual intervention, the classification method of weighted sparse representation-based classifier is used to make the final prediction. it is worthwhile to highlight several aspects of the proposed approach based on the experiments results here. the outstanding prediction performance shows that continuous wavelet transformation can cooperate well with the local binary pattern histogram fourier for protein feature extraction. the comparison result of wsrc versus svm demonstrates that wsrc can be well combined with graph-based feature extraction method and the use of cw-lbp may help wsrc give a full play to its function. it is worth noting that wsrc could yield stable and satisfactory prediction performance by keeping the same parameters in all experiment. compared with other conventional classifiers including svm, wsrc has a valuable advantage that it doesn’t need much manual intervention to adjust the optimal parameters and therefore has great potential to be applied to the large-scale prediction for new ppis. it is known that approaches using ensemble classifier usually achieve more accurate and robust performance than the methods using single classifier. however, using the single classifier, our proposed model obtains good performance similar to those obtained by the methods using ensemble classifier such as boosting. from these comparisons, it is demonstrated that the wsrc-based model combining the continuous wavelet transform descriptor and pseaa composition can improve the prediction accuracy compared with current state-of-the-art classification mothods.  the growing demand for ppis knowledge is promoting the development of studies on computational methods for predicting ppis. in this paper, we propose a new ppis prediction model only using the information of protein sequences. since hydrophilic interaction plays an important role in the process of protein interactions, we consider the hydrophobic property of amino acids in the process of feature extraction by transforming protein sequences into numerical ones. we then adopted continuous wavelet descriptors and chou’s pseudo amino acid composition, which aims at capturing the continuous and discrete information from the hydrophobic sequences. besides, weighted sparse representation based classifier was used as the sample classification model due to its advantages of low manual intervention in parameter adjustion and good cooperation with features. results obtained from our experiments have shown that it is a good attempt to represent proteins using graphic texture extraction approaches. our proposed method is feasible and effective. when performed on the saccharomyces cerevisiae, human and h.pylori datasets, the proposed method achieved promising results with high average accuracies of %, % and % respectively. 
therefore, computational methods for predicting ppis have an important role. high-throughput biological technologies can be used to identify new ppis, but they are expensive, time-consuming, and tedious. for the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting ppis. therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence. protein-protein interactions are essential to most biological processes. protein-protein interactionsprotein sequencecontinuous wavelet transformsparse representation based classifierthe 27th international conference on genome informatics shanghai, china 3- october http://admis.fudan.edu.cn/giw2016/issue-copyright-statement© the author 2017 in addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and wsrc. this method, cross-validated on the ppis datasets of saccharomyces cerevisiae, human and h. pylori, achieves an excellent results with accuracies as high as %, % and % respectively, significantly better than previously proposed methods. the outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. the main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and chou’s pseudo amino acid composition, and from adopting weighted sparse representation based classifier.
 protein-protein interactions are essential to most biological processes. since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about ppi network. high-throughput biological technologies can be used to identify new ppis, but they are expensive, time-consuming, and tedious. therefore, computational methods for predicting ppis have an important role. for the past years, an increasing number of computational methods such as protein structure-based approaches have been proposed for predicting ppis. the major limitation in principle of these methods lies in the prior information of the protein to infer ppis. therefore, it is of much significance to develop computational methods which only use the information of protein amino acids sequence.  here, we report a highly efficient approach for predicting ppis. the main improvements come from the use of a novel protein sequence representation by combining continuous wavelet descriptor and chou’s pseudo amino acid composition, and from adopting weighted sparse representation based classifier. this method, cross-validated on the ppis datasets of saccharomyces cerevisiae, human and h. pylori, achieves an excellent results with accuracies as high as %, % and % respectively, significantly better than previously proposed methods. extensive experiments are performed to compare the proposed method with state-of-the-art support vector machine classifier.  the outstanding results yield by our model that the proposed feature extraction method combing two kinds of descriptors have strong expression ability and are expected to provide comprehensive and effective information for machine learning-based classification models. in addition, the prediction performance in the comparison experiments shows the well cooperation between the combined feature and wsrc. thus, the proposed method is a very efficient method to predict ppis and may be a useful supplementary tool for future proteomics studies. keywords protein-protein interactionsprotein sequencecontinuous wavelet transformsparse representation based classifierthe 27th international conference on genome informatics shanghai, china 3- october http://admis.fudan.edu.cn/giw2016/issue-copyright-statement© the author 2017 