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.