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