@Article{JICS-12-026,
author = {ChunmingXu},
title = {Application of locality sensitive discriminant analysis to predict protein fold pattern},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {12},
number = {1},
pages = {026--032},
abstract = {Predicting protein-folding patterns is a challenge due to the complex structure of proteins. Many 
sequence  encoding  schemes  have  been  proposed  to  extract  the  features  of  pro-tein  sequences,  and  these 
features are often fused to form a new combined feature set so that it can contain various useful information. 
However, there usually has redundant information in the combined features. In this paper, a novel approach, 
LSDA-SVM,  is  proposed  to  predict  pro-tein  fold  pattern.  Firstly,  protein  samples  are  represented  by  the 
pseudo  amino  acid  composition  (PseAAC),  pair  wise  feature  (PF)  and  the  others  five  types  of  protein 
sequence  information,  and  these  features  are  further  combined  to  form  a  new  feature  set.  Secondly,  the 
locality sensitive discriminant analysis (LSDA) is employed to extract the more discriminant features. Finally, 
the  support  vector  machine  (SVM)  is  employed  to  classify  the  protein  sequences.  Experimental  results 
demonstrate the effectiveness of the proposed algorithm. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22495.html}
}