Establishing reliable miRNA-cancer association network based on text-mining method.

Establishing reliable miRNA-cancer association network based on text-mining method.

Li Lun; Hu Xingchi; Yang Zhaowan; Jia Zhenyu; Fang Ming; Zhang Libin; Zhou Yanhong

Computational and mathematical methods in medicine

2014

1905-7

Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification.

Algorithms; Area Under Curve; Computational Biology/*methods; Data Mining/*methods; False Positive Reactions; Gene Expression Profiling/methods; Gene Expression Regulation; Gene Regulatory Networks; Humans; MicroRNAs/*genetics/*metabolism; Neoplasms/*genetics/metabolism; Neoplastic; Probability; Reproducibility of Results

Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).

746979–746979

2014