@article{(Open Science Index):https://publications.waset.org/pdf/11527,
	  title     = {Selecting Negative Examples for Protein-Protein Interaction},
	  author    = {Mohammad Shoyaib and  M. Abdullah-Al-Wadud and  Oksam Chae},
	  country	= {},
	  institution	= {},
	  abstract     = {Proteomics is one of the largest areas of research for
bioinformatics and medical science. An ambitious goal of proteomics
is to elucidate the structure, interactions and functions of all proteins
within cells and organisms. Predicting Protein-Protein Interaction
(PPI) is one of the crucial and decisive problems in current research.
Genomic data offer a great opportunity and at the same time a lot of
challenges for the identification of these interactions. Many methods
have already been proposed in this regard. In case of in-silico
identification, most of the methods require both positive and negative
examples of protein interaction and the perfection of these examples
are very much crucial for the final prediction accuracy. Positive
examples are relatively easy to obtain from well known databases. But
the generation of negative examples is not a trivial task. Current PPI
identification methods generate negative examples based on some
assumptions, which are likely to affect their prediction accuracy.
Hence, if more reliable negative examples are used, the PPI prediction
methods may achieve even more accuracy. Focusing on this issue, a
graph based negative example generation method is proposed, which
is simple and more accurate than the existing approaches. An
interaction graph of the protein sequences is created. The basic
assumption is that the longer the shortest path between two
protein-sequences in the interaction graph, the less is the possibility of
their interaction. A well established PPI detection algorithm is
employed with our negative examples and in most cases it increases
the accuracy more than 10% in comparison with the negative pair
selection method in that paper.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {9},
	  year      = {2009},
	  pages     = {2163 - 2167},
	  ee        = {https://publications.waset.org/pdf/11527},
	  url   	= {https://publications.waset.org/vol/33},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 33, 2009},
	}