Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins.

DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase, prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of the conventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years, a variety of machine learning-based methods that have been used on a large scale for large-scale screening of DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparse representation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solve nonlinear classification problems. And L2,1-matrix norm is introduced to get sparse solution of model. To evaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRC achieves better performance in experimental results.