@article{Wang2010,
abstract = {Semantic similarity scores for protein pairs are widely applied in functional genomic researches for finding functional clusters of proteins, predicting protein functions and protein-protein interactions, and for identifying putative disease genes. However, because some proteins, such as those related to diseases, tend to be studied more intensively, annotations are likely to be biased, which may affect applications based on semantic similarity measures. Thus, it is necessary to evaluate the effects of the bias on semantic similarity scores between proteins and then find a method to avoid them.},
author = {Wang, Jing and Zhou, Xianxiao and Zhu, Jing and Zhou, Chenggui and Guo, Zheng},
doi = {10.1186/1471-2105-11-290},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Amino Acid,Cluster Analysis,Computational Biology,Computational Biology: methods,Disease,Disease: genetics,Genome,Humans,Proteins,Proteins: chemistry,Proteins: genetics,Proteins: metabolism,SML-LIB-BIBLIO,Sequence Homology,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jan,
pages = {290},
pmid = {20509916},
title = {{Revealing and avoiding bias in semantic similarity scores for protein pairs.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2903568\&tool=pmcentrez\&rendertype=abstract},
volume = {11},
year = {2010}
}