Discovering breast cancer drug candidates from biomedical literature

Authors:
Jiao Li;Xiaoyan Zhu;Jake Yue Chen
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;Indiana Center for Systems Biology and Personalized Medicine, Indiana University, School of Informatics, Indianapolis, IN 46202, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

Citing 4
Cited 2

Text mining: generating hypotheses from MEDLINE

Journal of the American Society for Information Science and Technology
Mining MEDLINE for implicit links between dietary substances and diseases

Bioinformatics
An architecture for biological information extraction and representation

Bioinformatics
Improved biomedical document retrieval system with PubMed term statistics and expansions

International Journal of Computational Intelligence in Bioinformatics and Systems Biology

Towards a database for genotype-phenotype association research: mining data from encyclopaedia

International Journal of Data Mining and Bioinformatics
Predicting human microRNA-disease associations based on support vector machine

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

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.