@article{Kundu_Backofen-Effic_Semi_Learn-2017,
author = {Kundu, Kousik and Backofen, Rolf},
title = {An {Efficient} {Semi}-supervised {Learning} {Approach} to 
         {Predict} {SH2} {Domain} {Mediated} {Interactions}},
journal = {Methods Mol Biol},
year = {2017},
doi = {10.1007/978-1-4939-6762-9_6},
volume = {1555},
user = {backofen},
pmid = {28092029},
pages = {83-97},
number = {},
issn = {1064-3745},
abstract = {Src homology 2 (SH2) domain is an important subclass of 
            modular protein domains that plays an indispensable role in 
            several biological processes in eukaryotes. SH2 domains 
            specifically bind to the phosphotyrosine residue of their 
            binding peptides to facilitate various molecular functions. 
            For determining the subtle binding specificities of SH2 
            domains, it is very important to understand the intriguing 
            mechanisms by which these domains recognize their target 
            peptides in a complex cellular environment. There are 
            several attempts have been made to predict SH2-peptide 
            interactions using high-throughput data. However, these 
            high-throughput data are often affected by a low signal to 
            noise ratio. Furthermore, the prediction methods have 
            several additional shortcomings, such as linearity problem, 
            high computational complexity, etc. Thus, computational 
            identification of SH2-peptide interactions using 
            high-throughput data remains challenging. Here, we propose a 
            machine learning approach based on an efficient 
            semi-supervised learning technique for the prediction of 51 
            SH2 domain mediated interactions in the human proteome. In 
            our study, we have successfully employed several strategies 
            to tackle the major problems in computational identification 
            of SH2-peptide interactions.}
}