Title: Discovering breast cancer drug candidates from biomedical literature
Authors: Jiao Li, Xiaoyan Zhu, Jake Yue Chen
Addresses: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. ' State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. ' Indiana Center for Systems Biology and Personalized Medicine, Indiana University, School of Informatics, Indianapolis, IN 46202, USA
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.
Keywords: biomedical text mining; structured data mining; drug identification; breast cancer; disease-specific drug candidates; disease-specific drugs; drug discovery; drug–protein association mining; cancer drugs; pharmacological profiles; bioinformatics.
DOI: 10.1504/IJDMB.2010.033519
International Journal of Data Mining and Bioinformatics, 2010 Vol.4 No.3, pp.241 - 255
Published online: 02 Jun 2010 *
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