title: GO-function: deriving biologically relevant functions from statistically significant functions.
abstract: In high-throughput studies of diseases, terms enriched with disease-related genes based on Gene Ontology (GO) are routinely found. However, most current algorithms used to find significant GO terms cannot handle the redundancy that results from the dependencies of GO terms. Simply based on some numerical considerations, current algorithms developed for reducing this redundancy may produce results that do not account for biologically interesting cases. In this article, we present several rules used to design a tool called GO-function for extracting biologically relevant terms from statistically significant GO terms for a disease. Using one gene expression profile for colorectal cancer, we compared GO-function with four algorithms designed to treat redundancy. Then, we validated results obtained in this data set by GO-function using another data set for colorectal cancer. Our analysis showed that GO-function can identify disease-related terms that are more statistically and biologically meaningful than those found by the other four algorithms.
authors.affiliation: Bioinformatics Centre, Key Laboratory for NeuroInformation of Ministry of Education and School of Life Science and Technology, University of Electronic Science and Technology of China.
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.forename: Xianxiao
authors.initials: X
authors.lastname: Zhou
authors.forename: Jing
authors.initials: J
authors.lastname: Zhu
authors.forename: Yunyan
authors.initials: Y
authors.lastname: Gu
authors.forename: Wenyuan
authors.initials: W
authors.lastname: Zhao
authors.forename: Jinfeng
authors.initials: J
authors.lastname: Zou
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D015179:Colorectal Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling