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XGSA: A statistical method for cross-species gene set analysis
Author(s) -
Djordje Djordjevic,
Kenro Kusumi,
Joshua W. K. Ho
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw428
Subject(s) - homology (biology) , gene , computational biology , gene prediction , biology , set (abstract data type) , genetics , genome , computer science , programming language
Gene set analysis is a powerful tool for determining whether an experimentally derived set of genes is statistically significantly enriched for genes in other pre-defined gene sets, such as known pathways, gene ontology terms, or other experimentally derived gene sets. Current gene set analysis methods do not facilitate comparing gene sets across different organisms as they do not explicitly deal with homology mapping between species. There lacks a systematic investigation about the effect of complex gene homology on cross-species gene set analysis.

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