DocumentCode :
3742500
Title :
Using improved K-nearest neighbor method to identify anti-and pro-apoptosis proteins
Author :
Zhen-He Yan;Ying-Li Chen;Jin-Tao Zhao
Author_Institution :
Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, China
fYear :
2015
Firstpage :
554
Lastpage :
559
Abstract :
Since the apoptosis protein plays an important role in understanding the mechanism of programmed cell death, so further to reveal the mechanism of subclass for apoptosis can bring more insights to their function. Here, our group established a dataset included 239 anti-apoptosis proteins and 222 pro-apoptosis proteins in our previous work. The extraction of information based on sequence information, gene ontology information and evolution information. Finally we proposed a mean value k-nearest neighbor (MKNN) algorithm. The results of MKNN indicated that the decision-making method of mean value is distinctly superior to the traditional decision-making method of majority vote. Meanwhile, we also listed the result of support vector machine (SVM) and k-nearest neighbor (KNN) to compare with our method. Then jackknife tests show that improved method is robust, useful and reliable for predicting the subcellular location of protein.
Keywords :
"Support vector machines","Amino acids","Decision making","Ontologies","Protein sequence","Databases"
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2015 8th International Conference on
Type :
conf
DOI :
10.1109/BMEI.2015.7401566
Filename :
7401566
Link To Document :
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