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Open Mobile Search. All Examples Functions Apps. Toggle navigation. Trials Product Updates. Their method yielded a prediction accuracy of In our previous works, we also obtained good prediction performance by using autocorrelation descriptors and correlation coefficient, respectively [ 8 , 17 ].
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The general trend in current study for predicting PPIs has focused on high accuracy but has not considered the time taken to train the classification models, which should be an important factor of developing a sequence-based method for predicting PPIs because the total number of possible PPIs is very large.
Therefore some computational models with high classification accuracy may not be satisfactory when considering the trade-off between the classification accuracy and the time for training the models. Recently, Huang et al. Previous works shown that ELM provides efficient unified solutions to generalized feed-forward networks including kernel learning.
N-terminal Amino Acid Sequence Analysis by MALDI-TOF MS : SHIMADZU (Shimadzu Corporation)
Consequently, ELM offers significant advantages such as fast learning speed, ease of implementation, and least human intervention. ELM has good potential as a viable alternative technique for large-scale computing and artificial intelligence. On the other hand, single ELM model is sometime difficult to achieve a satisfactory performance for the complex processes with strong nonlinearity, time variant and highly uncertainty. Ensemble ELM methods have received special attentions because it can improve the accuracy of predictor and achieve better stability through training a set of models and then combining them for final predictions [ 22 — 24 ].
For example, Lan et al. Zhao et al. In this study, an ensemble ELM model was built to predict the protein interactions. Previous works have pointed out that using feature selection or feature extraction before conducting the classification tasks can improve the classification accuracy[ 26 ]. Here, we attempt to examine the effectiveness of the dimensionality reduction technique before constructing the ELM classifier for the PPI prediction.
Principal component analysis PCA is utilized to do the feature extraction which projects the original feature space into a new space, on which the ELM is used to perform the prediction task. In this study, we report a new sequence-based method for the prediction of protein-protein interactions from amino acid sequences with ensemble ELM and PCA aiming at improving the efficiency and effectiveness of the classification accuracy.
Secondly, in order to reduce the computational complexity and enhance the overall accuracy of the predictor, an effective feature reduction method PCA is employed to extract the most discriminative new feature subset. Finally, ELM is chosen as the weak learning machine and the ensemble ELM classifier is constructed using the vectors of resulting feature subset as input.
Protein and Amino Acid Sequence Analysis
To evaluate the performance, the proposed method was applied to Saccharomyces cerevisiae PPI data. The prediction model was also assessed using the independent dataset of the Escherichia coli PPIs and yielded In this section, we first discuss the biological datasets and evaluation strategies used in performance comparisons. Next we present results for comparing the PCA-EELM method to state-of-the-art classifier for predicting protein interaction pairs in yeast. We evaluated the proposed method with the dataset of physical protein interactions from yeast used in the study of Guo et al.
The non-interacting protein pairs were generated from pairs of proteins whose sub-cellular localizations are different. The whole dataset consists of protein pairs, where half are from the positive dataset and half are from the negative dataset. To measure the performance of the proposed method, we adopted 5-fold cross validation and four parameters, the overall prediction accuracy Accu. They are defined as follows:.
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MCC denotes Mathews correlation coefficient. All the simulations were carried out on a computer with 3. All ELM in the ensemble classifier had the same number of hidden layer neurons but different random hidden layer weights and output layer weights. Ensemble ELM models were built via the stratified 5-fold cross-validation procedure through increasing gradually the number of hidden neurons from 20 to in interval of The best number of neurons was adapted to create the training model.
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The sigmoid activation function was used to compute the hidden layer output matrix. The final model was an ensemble of 15 extreme learning machines, and the outputs of ensemble ELM model were determined by combining the outputs of the each individual ELM by majority voting. In order to evaluate the prediction ability of our ELM classifiers, we also implemented a Support Vector Machine SVM learning algorithm which is thought of as the state-of-the-art classifier.
To reduce the bias of training and testing data, a 5-fold cross-validation technique is adopted. More specifically, the dataset is divided into 5 subsets, and the holdout method is reiterated 5 times. Each time four of the five subsets are put together as the training dataset, and the other one subset is utilized for testing the model. Thus five models were generated for the five sets of data. It can be observed from Table 1 that SVM shows good prediction accuracy in the range of For ensmble ELM, high prediction accuracy in the range of To better investigate the prediction ability of our model, we also calculated the values of Sensitivity, Precision, and MCC.
From Table 1 , we can see that our model gives good prediction performance with an average Sens. Further, it can also be seen in the Table 1 that the standard deviation of sensitivity, precision, accuracy and MCC are as low as 0. Therefore, we can see clearly that PCA-EELM model is a much more appropriate method for predicting new protein interactions compared with the other methods.
Consequently, it makes us be more convinced that the proposed PCA-EELM based method can be very helpful in assisting the biologist to assist in the design and validation of experimental studies and for the prediction of interaction partners. All the analysis shows that our model is an accurate and fast method for the prediction of PPIs.
In order to highlight the advantage of our model, it was also tested by Helicobacter pylori dataset. The H. This dataset gives a comparison of proposed method with other previous works including phylogenetic bootstrap[ 28 ], signature products[ 27 ], HKNN[ 29 ], ensemble of HKNN[ 30 ] and boosting[ 17 ]. The results of 10 fold cross-validation over six different methods are shown in Table 2. The average prediction performance, i.
It shows that the prediction results for PCA-EELM predictor and the ensemble of HKNN, outperforms other state-of-the-art methods, which highlight that a multiple classifier system is more accurate and robust than a single classifier. We also observed that the proposed method clearly achieves better results compared to other multiple classifier systems i. In this paper, we have developed an efficient and fast technique for predicting protein interactions from protein amino acids sequences by combining ensemble ELM with PCA.
The main aim of the proposed method is to employ the unique features of ELM classifier including better generalization performance, fast learning speed, simpler and without tedious and time-consuming parameter tuning to predict new protein interactions. In order to remove the noise and irrelevant features which affect the protein prediction performance, the PCA was utilized for feature reduction before conducting the ensemble ELM classifier.
Experimental results demonstrated that the proposed method performed significantly well in distinguishing interacting and non-interacting protein pairs.
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The experimental results showed that our method significantly outperformed PCA-SVM in terms of classification accuracy with shorter run time. The architecture is shown in Figure 1. Our method to predict the PPIs depends on three steps: 1 Represent protein pairs as a vector by using the proposed four kinds of protein sequence descriptors; 2 Principal component analysis is utilized to do the feature reduction; 3 Ensemble ELM is used to perform the protein interaction prediction tasks.
In the second stage, dimension reduction is obtained using PCA to project the original feature space into a new space. In the third stage, new feature sets are fed into the ensemble ELM classifier for training an optimal model, meanwhile the number of hidden neurons is chosen which can obtain the most accurate results. Finally, the predict model conducts the protein interaction prediction tasks using the most discriminative new feature set and the optimal parameters.
To use machine learning methods to predict PPIs from protein sequences, one of the most important computational challenges is to extract feature vectors from protein sequences in which the important information content of proteins is fully encoded. In this study, four kinds of feature representation methods including Auto Covariance AC , Conjoint triad CT , Local descriptor LD and Moran autocorrelation are employed to transform the protein sequences into feature vectors. Given a protein sequence, auto covariance AC accounts for the interactions between amino acids with a certain number of amino acids apart in the sequence, so this method takes neighbouring effect into account and makes it possible to discover patterns that run through entire sequences[ 9 ].
Here, six sequence-based physicochemical properties of amino acid were chosen to reflect the amino acids characteristics. Table 3 showed the values of the six physicochemical properties for each amino acid. By this means, the amino acid residues were first translated into numerical values representing physicochemical properties.
Then they were normalized to zero mean and unit standard deviation SD according to Equation 5 :. Then each protein sequence was translated into six vectors with each amino acid represented by the normalized values. Then auto covariance was used to transform these numerical sequences into uniform matrices. To represent a protein sample P with length L , the AC variables are calculated according to Equation 6 :.
After each protein sequence was represented as a vector of AC variables, a protein pair was characterized by concatenating the vectors of two proteins in this protein pair.
N- and C-Terminal Amino Acid Analysis
Conjoint triad CT considers the properties of one amino acid and its vicinal amino acids and regards any three continuous amino acids as a unit [ 11 ]. Thus, the triad can be differentiated according to the classes of amino acid. The PPI information of protein sequence can be projected into a homogeneous vector space by counting the frequency of each triad type. It should be noted that before using such feature representation method, the 20 amino acids has been clustered into seven classes according to the dipoles and volumes of the side chains.
The classification of amino acids is listed in Table 4. Finally, the descriptors of two proteins were concatenated and a total dimensional vector has been built to represent each protein pair. Local descriptor LD is an alignment-free approach and its effectiveness depends largely on the underlying amino acid groups [ 31 ].
To reduce the complexity inherent in the representation of the 20 standard amino acids, we firstly clustered it into seven functional groups based on the dipoles and volumes of the side chains see Table 4 for details. Then three local descriptors, Composition C , Transition T and Distribution D which is based on the variation of occurrence of functional groups of amino acids within the primary sequence of the protein are calculated.
C stands for the composition of each amino acid group along a local region. T represents the percentage frequency with which amino acid in one group is followed by amino acid in another group. In total there would be 63 features 7 composition, 21 transition, 35 distribution if they were computed from the whole amino acid sequence. However, in order to better capture continuous and discontinuous PPI information from the sequence, we split each protein into 10 local regions A-J of varying length and composition as follows: Regions A, B, C and D are obtained by dividing the entire protein sequence into four equal-length regions.