TY - GEN
T1 - Discovering biological processes and side effects relationship using the process-drug-side effect network
AU - Lee, Sejoon
AU - Song, Min
AU - Lee, Doheon
PY - 2010
Y1 - 2010
N2 - The side effect of drugs often results from a response to the unintended target of a drug. Recently there have been researches identifying targets of known drugs based on the side effect information. These researches, however, did not consider the association of drugs both with targets and with biological processes. The recent development of a database of the side effects, SIDER, is a first step to provide the relationship between drugs and side effects. In this paper, we propose a novel process-drug-side effect network that discovers the relationship between biological processes and side effects. The multi-level network (the process-drug-side effect network) is built by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in two ways: 1) how many biological processes discovered by our method are found in co-occurred GO terms with biological processes extracted from the PubMed records by a text mining technique. 2) whether there is the performance improvement by limiting response processes by drugs sharing the same side effect only to frequent ones. The experimental results show that our process-drug-side effect network is able to discover meaningful relationships between biological processes and side effects in an efficient manner.
AB - The side effect of drugs often results from a response to the unintended target of a drug. Recently there have been researches identifying targets of known drugs based on the side effect information. These researches, however, did not consider the association of drugs both with targets and with biological processes. The recent development of a database of the side effects, SIDER, is a first step to provide the relationship between drugs and side effects. In this paper, we propose a novel process-drug-side effect network that discovers the relationship between biological processes and side effects. The multi-level network (the process-drug-side effect network) is built by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in two ways: 1) how many biological processes discovered by our method are found in co-occurred GO terms with biological processes extracted from the PubMed records by a text mining technique. 2) whether there is the performance improvement by limiting response processes by drugs sharing the same side effect only to frequent ones. The experimental results show that our process-drug-side effect network is able to discover meaningful relationships between biological processes and side effects in an efficient manner.
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U2 - 10.1145/1871871.1871878
DO - 10.1145/1871871.1871878
M3 - Conference contribution
AN - SCOPUS:78651340377
SN - 9781450303828
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 31
EP - 39
BT - Proc. of the ACM 4th International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10
T2 - 4th International Workshop on Data and Text Mining in Biomedical Informatics, DTMBIO'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10
Y2 - 26 October 2010 through 30 October 2010
ER -