TY - GEN
T1 - RGRank
T2 - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012
AU - Taha, Kamal
AU - Al omouz, Dirar Mohammad
AU - Al-Muhairi, Hassan
PY - 2012
Y1 - 2012
N2 - Biologists often need to know the set S' of genes that are semantically related to a given set S of genes. However, most current similarity measures determine the semantic (biological meaning) similarities between the set S rather than identifying the set S̀ Moreover, these similarity measures determine the semantic similarities between the genes based solely on the proximity of their GO term annotations to each other in GO graph while overlooking the structural dependencies between these terms, which may lead to lower recall and precision of results. We propose in this paper a search engine called RGRank, which overcomes the limitations of current similarity measures outlined above as follows: (1) Given a set S of genes, RGRank would return a set Ś of genes, where each gene in S' is semantically related to each gene in S, (2) RGRank ranks gene results by relevance to their semantic similarities to the input genes, and (3) RGRank employs the concept of existence dependency to determine the structural dependencies among the GO terms annotating a given set of gene. We evaluated RGRank experimentally and compared it with three existing methods. Results showed marked improvement.
AB - Biologists often need to know the set S' of genes that are semantically related to a given set S of genes. However, most current similarity measures determine the semantic (biological meaning) similarities between the set S rather than identifying the set S̀ Moreover, these similarity measures determine the semantic similarities between the genes based solely on the proximity of their GO term annotations to each other in GO graph while overlooking the structural dependencies between these terms, which may lead to lower recall and precision of results. We propose in this paper a search engine called RGRank, which overcomes the limitations of current similarity measures outlined above as follows: (1) Given a set S of genes, RGRank would return a set Ś of genes, where each gene in S' is semantically related to each gene in S, (2) RGRank ranks gene results by relevance to their semantic similarities to the input genes, and (3) RGRank employs the concept of existence dependency to determine the structural dependencies among the GO terms annotating a given set of gene. We evaluated RGRank experimentally and compared it with three existing methods. Results showed marked improvement.
UR - https://www.scopus.com/pages/publications/84874067461
U2 - 10.1109/ICCSE.2012.46
DO - 10.1109/ICCSE.2012.46
M3 - Conference contribution
AN - SCOPUS:84874067461
SN - 9780769549149
T3 - Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012
SP - 281
EP - 288
BT - Proceedings - 15th IEEE International Conference on Computational Science and Engineering, CSE 2012 and 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC 2012
Y2 - 5 December 2012 through 7 December 2012
ER -