Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method | Zendy

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

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Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

Author(s) -

Lun Li,

Xingchi Hu,

Zhaowan Yang,

Zhenyu Jia,

Ming Fang,

Libin Zhang,

Yanhong Zhou

Publication year - 2014

Publication title -

computational and mathematical methods in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.462

H-Index - 48

eISSN - 1748-6718

pISSN - 1748-670X

DOI - 10.1155/2014/746979

Subject(s) - microrna , cancer , identification (biology) , computational biology , disease , bioinformatics , biology , medicine , gene , genetics , pathology , botany

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.

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