Application of density similarities to predict membrane protein types based on pseudo-amino acid composition

Mahdavi, Abbas; Jahandideh, Samad

doi:10.1016/j.jtbi.2011.01.048

Cited by 21 publications

(4 citation statements)

References 45 publications

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“…The accuracy of the ensemble classifier is 86.2%, which accounts for its superiority as against the individual classifiers. The predicted results of Mem-Ens-SAAC are higher than the predicted output of MemType-2L (Chou and Shen 2007a) and (Mahdavi and Jahandideh 2011), and are the best results reported so far.…”

Section: Prediction Performance Using Dataset2mentioning

confidence: 48%

“…The feature extraction strategy used for this dataset is SAAC and we have evaluated the performance of classifiers using jackknife test. In Table 5, the performance of individual and ensemble classifier for overall and each membrane protein type's is shown and compared with that of MemType-2L (Chou and Shen 2007a) and (Mahdavi and Jahandideh 2011). Among the individual classifiers, SVM yields the highest accuracy of 84.2%.…”

Section: Prediction Performance Using Dataset2mentioning

confidence: 99%

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Prediction of membrane proteins using split amino acid and ensemble classification

2011

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Knowledge of the types of membrane protein provides useful clues in deducing the functions of uncharacterized membrane proteins. An automatic method for efficiently identifying uncharacterized proteins is thus highly desirable. In this work, we have developed a novel method for predicting membrane protein types by exploiting the discrimination capability of the difference in amino acid composition at the N and C terminus through split amino acid composition (SAAC). We also show that the ensemble classification can better exploit this discriminating capability of SAAC. In this study, membrane protein types are classified using three feature extraction and several classification strategies. An ensemble classifier Mem-EnsSAAC is then developed using the best feature extraction strategy. Pseudo amino acid (PseAA) composition, discrete wavelet analysis (DWT), SAAC, and a hybrid model are employed for feature extraction. The nearest neighbor, probabilistic neural network, support vector machine, random forest, and Adaboost are used as individual classifiers. The predicted results of the individual learners are combined using genetic algorithm to form an ensemble classifier, Mem-EnsSAAC yielding an accuracy of 92.4 and 92.2% for the Jackknife and independent dataset test, respectively. Performance measures such as MCC, sensitivity, specificity, F-measure, and Q-statistics show that SAAC-based prediction yields significantly higher performance compared to PseAA- and DWT-based systems, and is also the best reported so far. The proposed Mem-EnsSAAC is able to predict the membrane protein types with high accuracy and consequently, can be very helpful in drug discovery. It can be accessed at http://111.68.99.218/membrane.

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Section: Prediction Performance Using Dataset2mentioning

confidence: 48%

Section: Prediction Performance Using Dataset2mentioning

confidence: 99%

Prediction of membrane proteins using split amino acid and ensemble classification

2011

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“…PseAAC (pseudo amino acid composition) to prevent the protein sequence order and pattern data. [29]. PseAAC has to generate ordered 50-dimensional vector space for each sequence data to be involved in computational proteomics [20], and sequence length generate 1 dimensional vector space each samples.…”

Section: B Feature Extraction Methodsmentioning

confidence: 99%

Pseudo Amino Acid Feature-based Protein Function Prediction using Support Vector Machine and K-Nearest Neighbors

Deen¹,

Gyanchandani²

2020

IJACSA

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Bioinformatics facing the vital challenge in protein function prediction due to protein data are available in primary structure, an amino acid sequence. Every protein cell sequence length and size are in different sequence order. Protein is available in 20 amino acid sequence alphabetic order; however, the corresponding information of the membrane protein sequence is insufficient to capture the function and structures of a protein from primary sequence datasets. A challenging task to correctly identify protein structure and function from amino acid sequence. The basic principle of PseAAC (Pseudo Amino Acid Composition) is to generate a discrete number of every protein samples. In each protein, sequence length varies due to protein functions. Some protein sequence length is less than 50, and some are large. Due to this, different sizes of the amino acid sample are chances to lose sequence order information. PseAAC feature generates a fixed size descriptor value in vector space to overcome sequence information loss and is used to further systematic evolution. Therefore machine learning computational tool synthesizes accurate identification of structure and function class of membrane protein. In this study, SVM (Support Vector Machine) and KNN (K-nearest neighbors) based prediction classifier used to identifying membrane protein and their types.

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“…Reflecting the order of amino acid in the sequence and complementing AAC, PseAAC of protein sequence is considered to be the extension of it. Those features were widely applied to recognize types of uncharacterized membrane proteins [7] , [10] , [11] , [12] , [13] . Wang et al .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

et al. 2014

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Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.

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Application of density similarities to predict membrane protein types based on pseudo-amino acid composition

Cited by 21 publications

References 45 publications

Prediction of membrane proteins using split amino acid and ensemble classification

Prediction of membrane proteins using split amino acid and ensemble classification

Pseudo Amino Acid Feature-based Protein Function Prediction using Support Vector Machine and K-Nearest Neighbors

Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

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