Dataset Information

Feature extraction method for proteins based on Markov tripeptide by compressive sensing.

ABSTRACT: BACKGROUND:In order to capture the vital structural information of the original protein, the symbol sequence was transformed into the Markov frequency matrix according to the consecutive three residues throughout the chain. A three-dimensional sparse matrix sized 20?×?20?×?20 was obtained and expanded to one-dimensional vector. Then, an appropriate measurement matrix was selected for the vector to obtain a compressed feature set by random projection. Consequently, the new compressive sensing feature extraction technology was proposed. RESULTS:Several indexes were analyzed on the cell membrane, cytoplasm, and nucleus dataset to detect the discrimination of the features. In comparison with the traditional methods of scale wavelet energy and amino acid components, the experimental results suggested the advantage and accuracy of the features by this new method. CONCLUSIONS:The new features extracted from this model could preserve the maximum information contained in the sequence and reflect the essential properties of the protein. Thus, it is an adequate and potential method in collecting and processing the protein sequence from a large sample size and high dimension.

SUBMITTER: Gao CF

PROVIDER: S-EPMC6006778 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Feature extraction method for proteins based on Markov tripeptide by compressive sensing.

Gao C F CF Wu X Y XY

BMC bioinformatics 20180618 1

<h4>Background</h4>In order to capture the vital structural information of the original protein, the symbol sequence was transformed into the Markov frequency matrix according to the consecutive three residues throughout the chain. A three-dimensional sparse matrix sized 20 × 20 × 20 was obtained and expanded to one-dimensional vector. Then, an appropriate measurement matrix was selected for the vector to obtain a compressed feature set by random projection. Consequently, the new compressive sen ...[more]

PMID: 29914376

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Project description:BackgroundThe diagnosis of cardiac abnormalities based on heart sound signal is a research hotspot in recent years. The early diagnosis of cardiac abnormalities has a crucial significance for the treatment of heart diseases.MethodsFor the sake of achieving more practical clinical applications of automatic recognition of cardiac abnormalities, here we proposed a novel fuzzy matching feature extraction method. First of all, a group of Gaussian wavelets are selected and then optimized based on a template signal. Convolutional features of test signal and the template signal are then computed. Matching degree and matching energy features between template signal and test signal in time domain and frequency domain are then extracted. To test performance of proposed feature extraction method, machine learning algorithms such as K-nearest neighbor, support vector machine, random forest and multilayer perceptron with grid search parameter optimization are constructed to recognize heart disease using the extracted features based on phonocardiogram signals.ResultsAs a result, we found that the best classification accuracy of random forest reaches 96.5% under tenfold cross validation using the features extracted by the proposed method. Further, Mel-Frequency Cepstral Coefficients of phonocardiogram signals combing with features extracted by our algorithm are evaluated. Accuracy, sensitivity and specificity of integrated features reaches 99.0%, 99.4% and 99.7% respectively when using support vector machine, which achieves the best performance among all reported algorithms based on the same dataset. On several common features, we used independent sample t-tests. The results revealed that there are significant differences (p < 0.05) between 5 categories.ConclusionIt can be concluded that our proposed fuzzy matching feature extraction method is a practical approach to extract powerful and interpretable features from one-dimensional signals for heart sound diagnostics and other pattern recognition task.

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Feature extraction method for proteins based on Markov tripeptide by compressive sensing.

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Feature extraction method for proteins based on Markov tripeptide by compressive sensing.

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