Paper Info
Title
An Effective Feature Fusion Method For Protein Sub-Nuclear Localization
Abstract
Protein sub-nuclear localization plays a significant role in life science research. Feature extraction is the key step in the processes of protein sub-nuclear localization, it should include useful and sufficient protein information as possible. However, the single feature extraction method often can't extract sufficient information. Thus, we propose a novel method by combining PseAAC with PSSM to solve this problem. First, PseAAC and PSSM are used to extract the information, respectively. Secondly, the KPCA is used to reduce the dimensionality of PseAAC and PSSM. Thirdly, the useful information of PseAAC and PSSM will be integrated by proposed method. At last, the integrated feature vectors are classified by Random Forest. The classification performance will be evaluated with four evaluation indexes by jackknife test. According to the experiments results, the proposed feature fusion method is effective for protein sub-nuclear localization.
Year
DOI
Venue
2018
10.1109/CISP-BMEI.2018.8633262
2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018)
Keywords
Field
DocType
Protein sub-nuclear localization, Kernel principal components analysis, PseAAC, PSSM
Feature vector,Feature fusion,Jackknife resampling,Pattern recognition,Computer science,Protein engineering,Feature extraction,Curse of dimensionality,Artificial intelligence,Random forest,Principal component analysis
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Liwen Wu102.70
Yi Xiang2214.73
Yikun Yang300.68
Shaowen Yao48626.85
Gang Xue5279.09
Qian Jiang6113.86
Xin Jin700.68