KSIBW: Predicting Kinase-Substrate Interactions Based on Bi-random Walk

Author(s):  
Canshang Deng ◽  
Qingfeng Chen ◽  
Zhixian Liu ◽  
Ruiqing Zheng ◽  
Jin Liu ◽  
...  
Author(s):  
Aparna Krishnan ◽  
Kristin Leskoske ◽  
Krystine Garcia-Mansfield ◽  
Ritin Sharma ◽  
Jessica Rusert ◽  
...  

FEBS Letters ◽  
2002 ◽  
Vol 520 (1-3) ◽  
pp. 156-160 ◽  
Author(s):  
Keykavous Parang ◽  
Jeffrey A. Kohn ◽  
S.Adrian Saldanha ◽  
Philip A. Cole

2019 ◽  
Vol 20 (2) ◽  
pp. 302 ◽  
Author(s):  
Jingzhong Gan ◽  
Jie Qiu ◽  
Canshang Deng ◽  
Wei Lan ◽  
Qingfeng Chen ◽  
...  

Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification.


2006 ◽  
Vol 16 (6) ◽  
pp. 668-675 ◽  
Author(s):  
Ron Bose ◽  
Marc A Holbert ◽  
Kerry A Pickin ◽  
Philip A Cole

Author(s):  
Michael G. Smith ◽  
Jason Ptacek ◽  
Michael Snyder

Sign in / Sign up

Export Citation Format

Share Document