DocumentCode :
3732156
Title :
Summary of Statistical Prediction of Posttranslational Modification Sites in Proteins
Author :
Li-Shu Wen;Cai-Lian Yao
Author_Institution :
Sch. of Sci., Shenyang Aerosp. Univ., Shenyang, China
fYear :
2015
Firstpage :
805
Lastpage :
808
Abstract :
Most proteins perform their functions after post-translational modifications (PTMs). Post-translational modifications play crucial roles in various cell functions and biological process. Computational identification of PTMs is an important way in contrast to experimental approach with its convenience. This review gave the process for prediction of Post-translational modification sites including benchmark dataset, feature construction, algorithm, evaluation measurement, and online web-server. Feature construction which reflects intrinsic correlation with proteins was emphasized mainly. Seven feature construction methods containing physiochemical properties and sequence information have been illustrated principally. Last nitro tyrosine was an application to design a site-specific predictor. The procedure was clearly described.
Keywords :
"Proteins","Peptides","Amino acids","Support vector machines","Yttrium","Benchmark testing","Machine learning algorithms"
Publisher :
ieee
Conference_Titel :
Intelligent Transportation, Big Data and Smart City (ICITBS), 2015 International Conference on
Type :
conf
DOI :
10.1109/ICITBS.2015.203
Filename :
7384149
Link To Document :
بازگشت