Discovering breast cancer drug candidates from biomedical literature.

Abstract

We developed a new paradigm with the ultimate goal of enabling disease-specific drug candidate discovery with molecular-level evidences generated from literature and prior knowledge. We showed how to implement the paradigm by building a prototype literature-mining framework and performing drug-protein association mining for breast cancer drug discovery. In a molecular pharmacology study of breast cancer, 79.2% of 729 enriched drugs in 'Organic Chemicals' category were validated to be disease-related, and the remaining 20.8% were also investigated as potential for future molecular therapeutics studies. 'Doxorubicin', 'Etoposide' and 'Paclitaxel' were identified as having similar pharmacological profiles to treat breast cancer.

• Authors: Jiao Li, Xiaoyan Zhu, Jake Yue Chen
• Journal: International journal of data mining and bioinformatics (Int J Data Min Bioinform) Vol. 4 Issue 3 Pg. 241-55 ( 2010) ISSN: 1748-5673 [Print] Switzerland
• PMID: 20681478 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Antineoplastic Agents
• Etoposide
• Doxorubicin
• Paclitaxel

Topics

• Antineoplastic Agents (therapeutic use)
• Breast Neoplasms (drug therapy)
• Data Mining (methods)
• Doxorubicin (therapeutic use)
• Drug Discovery (methods)
• Etoposide (therapeutic use)
• Female
• Humans
• Internet
• MEDLINE
• Paclitaxel (therapeutic use)
• Publications