Protein Sequence Comparison on the Connection Machine CM-2

2018 ◽  
pp. 99-107
Author(s):  
Robert Jones ◽  
Washington Taylor ◽  
Xiru Zhang ◽  
Jill P. Mesirov ◽  
Eric Lander
1989 ◽  
Vol 3 (4) ◽  
pp. 255-269 ◽  
Author(s):  
Eric Lander ◽  
Jill P. Mesirov ◽  
Washington Taylor

2005 ◽  
Vol 15 (3) ◽  
pp. 254-260 ◽  
Author(s):  
William R Pearson ◽  
Michael L Sierk

2016 ◽  
Vol 06 (02) ◽  
pp. 33-40 ◽  
Author(s):  
Jayanta Pal ◽  
Soumen Ghosh ◽  
Bansibadan Maji ◽  
Dilip Kumar Bhattacharya

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Lulu Yu ◽  
Yusen Zhang ◽  
Ivan Gutman ◽  
Yongtang Shi ◽  
Matthias Dehmer

Abstract We develop a novel position-feature-based model for protein sequences by employing physicochemical properties of 20 amino acids and the measure of graph energy. The method puts the emphasis on sequence order information and describes local dynamic distributions of sequences, from which one can get a characteristic B-vector. Afterwards, we apply the relative entropy to the sequences representing B-vectors to measure their similarity/dissimilarity. The numerical results obtained in this study show that the proposed methods leads to meaningful results compared with competitors such as Clustal W.


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