CMSENN: Computational Modification Sites with Ensemble Neural Network

Abstract

With the rapid development of high-through technology, vast amounts of protein molecular data has been generated, which is crucial to advance our understanding of biological organisms. An increasing number of protein post translation modification sites identification approaches have been designed and developed to detect such modification sites among the protein sequences. Nevertheless, these methods are merely suitable for one type of modification site, their performance deteriorate rapidly when applied to other types of modification sites’ prediction. In this paper, with the method of different types of neural network algorithm ensemble, a novel method, named CMSENN (http://121.250.173.184/) Computational Modification Sites with Ensemble Neural Network, was proposed to detect protein modification. The algorithm mainly consists of several steps: First, the predicted peptide sequences translate to the feature vectors. Second, the three types of employed amino acid residues properties should be normalized. Finally, various combination of features and classification model have been compared the performances with several current typical algorithms. The results demonstrate that the proposed model have well performance at the sensitivity, specificity, F1 score and Matthews correlation coefficient (MCC) value in the identification modification with the approach of the selected features and algorithm combination.