Finding relevant protein of ADRs using text mining in Taverna
4
1
Issued Date
2011-03
Resource Type
Language
eng
Rights
Mahidol University
Rights Holder(s)
Mahidol University
Bibliographic Citation
Tantayapirak P, Pornputtapong N, Thammarongtham C, Saithong T, Senachak J, Chumchua V. Finding relevant protein of ADRs using text mining in Taverna. 15th International Annual Symposium on Computational Science and Engineering (ANSCSE15); March 30-April 1. Bangkok: Bangkok University; 2011. p. 403-8
Suggested Citation
P.Tantayapirak, N.Pornputtapong, C.Thammarongtham, T.Saithong, J.Senachak, V.Chumchua Finding relevant protein of ADRs using text mining in Taverna. Tantayapirak P, Pornputtapong N, Thammarongtham C, Saithong T, Senachak J, Chumchua V. Finding relevant protein of ADRs using text mining in Taverna. 15th International Annual Symposium on Computational Science and Engineering (ANSCSE15); March 30-April 1. Bangkok: Bangkok University; 2011. p. 403-8. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/43863
Title
Finding relevant protein of ADRs using text mining in Taverna
Other Contributor(s)
King Mongkut's University of Technology Thonburi. Bioinformatics and Systems Biology Program
Chalmers University of Technology Sweden. Systems and Synthetic Biology Group
National Center for Genetic Engineering and Biotechnology. Biochemical Engineering and Pilot Plant Research and Development Unit (BEC)
Mahidol University. National Institute for Child and Family Development
Chalmers University of Technology Sweden. Systems and Synthetic Biology Group
National Center for Genetic Engineering and Biotechnology. Biochemical Engineering and Pilot Plant Research and Development Unit (BEC)
Mahidol University. National Institute for Child and Family Development
Abstract
Adverse Drug Reactions (ADRs) are the undesirable
reactions from use of medicinal products effecting protein through
drugs or their reactive metabolites binding. These are the fourth
leading cause of death in the US. More than nineteen drugs have
been withdrawn since 1998 because of their adverse reactions.
These incidents diminish the medical confidences and value of
pharmaceutical industry. Many attempts have been made to
explicate the relevance of proteins and ADRs which reveal their
underlying biological mechanisms. These will be useful for
adverse reactions prediction. However, the rapid growth in
published scientific literatures leads to the difficulty of being up
to date and figuring out the relevant data for researchers. Text
mining is a solution for this trouble by extracting information
from written resources. By implementing text mining and Taverna,
the text mining function is presented in a single graphical
workflow. There are 2,551 proteins that discovered to associated
with 83 Preferred Terms of hepatobiliary disorders. The collection
of these proteins and their relavence then will be connected with
ADRs and drug information. The relations might be used for
facilitating better understanding of the molecular mechanisms
of drug-induced ADRs and the rational drug discovery.