@inproceedings{b63899c3887f41ee895a86f131af39f0,
title = "GOseek: A gene ontology search engine using enhanced keywords",
abstract = "We propose in this paper a biological search engine called GOseek, which overcomes the limitation of current gene similarity tools. Given a set of genes, GOseek returns the most significant genes that are semantically related to the given genes. These returned genes are usually annotated to one of the Lowest Common Ancestors (LCA) of the Gene Ontology (GO) terms annotating the given genes. Most genes have several annotation GO terms. Therefore, there may be more than one LCA for the GO terms annotating the given genes. The LCA annotating the genes that are most semantically related to the given gene is the one that receives the most aggregate semantic contribution from the GO terms annotating the given genes. To identify this LCA, GOseek quantifies the contribution of the GO terms annotating the given genes to the semantics of their LCAs. That is, it encodes the semantic contribution into a numeric format. GOseek uses microarray experiment data to rank result genes based on their significance. We evaluated GOseek experimentally and compared it with a comparable gene prediction tool. Results showed marked improvement over the tool.",
author = "Kamal Taha",
year = "2013",
doi = "10.1109/EMBC.2013.6609797",
language = "British English",
isbn = "9781457702167",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1502--1505",
booktitle = "2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013",
address = "United States",
note = "2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 ; Conference date: 03-07-2013 Through 07-07-2013",
}