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

2018 ◽  
pp. 151-162
Author(s):  
Canshang Deng ◽  
Qingfeng Chen ◽  
Zhixian Liu ◽  
Ruiqing Zheng ◽  
Jin Liu ◽  
...  
Keyword(s):  
Random Walk ◽  
Substrate Interactions ◽  
Kinase Substrate

Predicting Kinase-Substrate Interactions in Medulloblastoma Subtypes

2020 ◽  
Author(s):  
Aparna Krishnan ◽  
Kristin Leskoske ◽  
Krystine Garcia-Mansfield ◽  
Ritin Sharma ◽  
Jessica Rusert ◽  
...  
Keyword(s):  
Substrate Interactions ◽  
Kinase Substrate

scholarly journals Development of photo-crosslinking reagents for protein kinase-substrate interactions

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

scholarly journals KSIMC: Predicting Kinase–Substrate Interactions Based on Matrix Completion

2019 ◽  
Vol 20 (2) ◽  
pp. 302 ◽  
Author(s):  
Jingzhong Gan ◽  
Jie Qiu ◽  
Canshang Deng ◽  
Wei Lan ◽  
Qingfeng Chen ◽  
...  
Keyword(s):  
Scientific Literature ◽  
State Of The Art ◽  
Matrix Completion ◽  
Computational Approach ◽  
Computational Method ◽  
Substrate Interactions ◽  
Kinase Substrate ◽  
Cellular Processes ◽  
Substrate Interaction ◽  
The Matrix

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.

Protein tyrosine kinase–substrate interactions

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

Kinase Substrate Interactions

2011 ◽  
pp. 201-212 ◽  
Author(s):  
Michael G. Smith ◽  
Jason Ptacek ◽  
Michael Snyder
Keyword(s):  
Substrate Interactions ◽  
Kinase Substrate
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