@inproceedings{8a138d8a76564a78aed7d8311b9394be,
title = "Using gene sets to identify putative drugs for breast cancer",
abstract = "The number of current anti-cancer drugs was limited and the response rates were also not high. To {"}?reposition{"} known drugs as anti-cancer drugs to increase the therapeutic efficiency, we presented a novel analysis framework to identify putative drugs for cancer. Using breast cancer as example, a {"}cancer - gene sets - drugs{"} network was constructed through two procedures. First, the {"}gene sets - drugs{"} network was built by applying the expression pattern of drugs for gene set enrichment analysis. Secondly, the breast cancer progression associated gene sets were identified by survival analysis of patient cohorts. By integrating the two results, 25 tumor progression associated gene sets and 360 putative anti-cancer drugs were identified. Our method has the ability to identify the {"}reposition{"} drugs and the potential affected mechanisms of tumor progression concurrently. It will be useful to speed up the development of anti-cancer drugs from bench to clinical application.",
keywords = "breast cancer, drug, gene set",
author = "Hsiao, {Tzu Hung} and Chen, {Hung I.Harry} and Yidong Chen and Chen, {Yu Heng} and Chuang, {Eric Y.}",
year = "2012",
doi = "10.1109/BIBM.2012.6392616",
language = "English (US)",
isbn = "9781467325585",
series = "Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012",
pages = "552--555",
booktitle = "Proceedings - 2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2012",
note = "2012 IEEE International Conference on Bioinformatics and Biomedicine, BIBM2012 ; Conference date: 04-10-2012 Through 07-10-2012",
}