title: A novel network-based method for measuring the functional relationship between gene sets.
abstract: BACKGROUND
  In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms.
  
  
  RESULTS
  We introduce a novel network-based method, designated corrected cumulative rank score (CCRS), which analyzes the functional communication and physical interaction between genes, and presents an easy-to-use web-based toolkit called GsNetCom to quantify the functional relationship between two gene sets. To evaluate the performance of our method in assessing the functional similarity between two gene sets, we analyzed the functional coherence of complexes in functional catalog and identified protein complexes in the same functional catalog. The results suggested that CCRS can offer a significant advance in addressing the functional relationship between different gene sets compared with several other available tools or algorithms with similar functionality. We also conducted the case study based on our method, and succeeded in prioritizing candidate leukemia-associated protein complexes and expanding the prioritization and analysis of cancer-related complexes to other cancer types. In addition, GsNetCom provides a new insight into the communication between gene modules, such as exploring gene sets from the perspective of well-annotated protein complexes.
  
  
  METHODS
  GsNetCom is a freely available web accessible toolkit at http://bioinfo.hrbmu.edu.cn/GsNetCom.
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors: Qianghu
authors: Q
authors: Wang
authors: Jie
authors: J
authors: Sun
authors: Meng
authors: M
authors: Zhou
authors: Haixiu
authors: H
authors: Yang
authors: Yan
authors: Y
authors: Li
authors: Xiang
authors: X
authors: Li
authors: Sali
authors: S
authors: Lv
authors: Xia
authors: X
authors: Li
authors: Yixue
authors: Y
authors: Li
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D046912:Multiprotein Complexes / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction; D012984:Software
chemical_list: D046912:Multiprotein Complexes