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 experimen

SUBMITTER: Gao CF 

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

REPOSITORIES: biostudies-literature