scholarly journals Efficient use of unlabeled data for protein sequence classification: a comparative study

2009 ◽  
Vol 10 (Suppl 4) ◽  
pp. S2 ◽  
Author(s):  
Pavel Kuksa ◽  
Pai-Hsi Huang ◽  
Vladimir Pavlovic
Keyword(s):  
Comparative Study ◽  
Protein Sequence ◽  
Unlabeled Data ◽  
Sequence Classification ◽  
Protein Sequence Classification

scholarly journals Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiuwen Cao ◽  
Lianglin Xiong
Keyword(s):  
Extreme Learning Machine ◽  
Protein Sequence ◽  
Protein Sequences ◽  
Activation Function ◽  
Majority Voting ◽  
Training Algorithms ◽  
Sequence Classification ◽  
Protein Sequence Classification ◽  
Learning Machine ◽  
Majority Voting Method

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.

scholarly journals Learned Random-Walk Kernels and Empirical-Map Kernels for Protein Sequence Classification

2009 ◽  
Vol 16 (3) ◽  
pp. 457-474 ◽  
Author(s):  
Renqiang Min ◽  
Anthony Bonner ◽  
Jingjing Li ◽  
Zhaolei Zhang
Keyword(s):  
Random Walk ◽  
Protein Sequence ◽  
Sequence Classification ◽  
Protein Sequence Classification

scholarly journals Mining for class-specific motifs in protein sequence classification

2013 ◽  
Vol 14 (1) ◽  
pp. 96 ◽  
Author(s):  
Satish M Srinivasan ◽  
Suleyman Vural ◽  
Brian R King ◽  
Chittibabu Guda
Keyword(s):  
Protein Sequence ◽  
Sequence Classification ◽  
Protein Sequence Classification
Sign in / Sign up

Export Citation Format

Share Document