title: Protein sequence classification with improved extreme learning machine algorithms.
abstract: Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.
authors.affiliation: Institute of Information and Control, Hangzhou Dianzi University, Zhejiang 310018, China.
authors.forename: Jiuwen
authors.identifier: 0000-0002-6480-5794
authors.initials: J
authors.lastname: Cao
authors.affiliation: School of Mathematics and Computer Science, Yunnan University of Nationalities, Kunming 650500, China ; School of Mathematics and Statistics, Yunnan University, Kunming 650091, China.
authors.forename: Lianglin
authors.identifier: 0000-0002-5802-0974
authors.initials: L
authors.lastname: Xiong
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins