@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},
  volume =	 {1555},
  number =	 {},
  pages =	 {83-97},
  user =	 {backofen},
  pmid =	 {28092029},
  doi = 	 {10.1007/978-1-4939-6762-9_6},
  issn = 	 {1064-3745},
  issn = 	 {1940-6029},
  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.}
}