TY - JOUR AU - Sun, Liang AU - Zhu, Yongnan AU - Mahmood, A. S. M. Ashique AU - Tudor, Catalina O. AU - Ren, Jia AU - Vijay-Shanker, K. AU - Chen, Jian AU - Schmidt, Carl J. PY - 2017 DA - 2017/05/04 TI - WebGIVI: a web-based gene enrichment analysis and visualization tool JO - BMC Bioinformatics SP - 237 VL - 18 IS - 1 AB - A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-017-1664-2 DO - 10.1186/s12859-017-1664-2 ID - Sun2017 ER -