Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoretical limit. In this paper, we present a novel method for protein secondary structure prediction based on a data partition and semi-random subspace method (PSRSM). Data partitioning is an important strategy for our method. First, the protein training dataset was partitioned into several subsets based on the length of the protein sequence. Then we trained base classifiers on the subspace data generated by the semi-random subspace method, and combined base classifiers by majority vote rule into ensemble classifiers on each subset. Multiple classifiers were trained on different subsets. These different classifiers were used to predict the secondary structures of different proteins according to the protein sequence length. Experiments are performed on 25PDB, CB513, CASP10, CASP11, CASP12, and T100 datasets, and the good performance of 86.38%, 84.53%, 85.51%, 85.89%, 85.55%, and 85.09% is achieved respectively. Experimental results showed that our method outperforms other state-of-the-art methods. Proteins play a key role in almost all biological processes; they are the basis of life. For example, they take part in maintaining the structural integrity of the cell, transport and storage of small molecules, catalysis, regulation, signaling, and the immune system. There are 20 different amino acids that form proteins in nature 1 . The amino acids of a protein are connected in sequence with the carboxyl group of one amino acid forming a peptide bond with the amino group of the next amino acid. Protein structure is essential for the understanding of protein function. In order to recognize the protein functions of proteins at a molecular level, it is sometimes necessary to determine their 3D structure. Accurately and reliably predicting structures from protein sequences is one of the most challenging tasks in computational biology 2 . Protein secondary structure prediction provides a significant first step toward tertiary structure prediction, as well as offering information about protein activity, relationships, and functions.
Based on a semi-random subspace method (PSRSM) and data partition in REF , Ma et al. presented a novel architecture that utilized Support Vector Machines (SVMs) as the base classifier.
Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method
Scientific Reports
Scientific Reports
Medicine Computer Science