Name
Abstract | ||
|---|---|---|
More attention has been being paid to combinatorial effects of drugs to treat complex diseases or to avoid adverse combinations of drug cocktail. Although drug interaction information has been increasingly accumulated, a novel approach like network-based method is needed to analyse that information systematically and intuitivelyBeyond focussing on drug-drug interactions, we examined interactions between functional drug groups. In this work, functional drug groups were defined based on the Anatomical Therapeutic Chemical (ATC) Classification System. We defined criteria whether two functional drug groups are related. Then we constructed the interaction network of drug groups. The resulting network provides intuitive interpretations. We further constructed another network based on interaction sharing ratio of the first network. Subsequent analysis of the networks showed that some features of drugs can be well described by this kind of interaction even for the case of structurally dissimilar drugs.Our networks in this work provide intuitive insights into interactions among drug groups rather than those among single drugs. In addition, information on these interactions can be used as a useful source to describe mechanisms and features of drugs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1752-0509-7-S3-S4 | BMC systems biology |
Keywords | Field | DocType |
bioinformatics,systems biology,biomedical research,algorithms | Computer science,Drug interaction,Systems biology,Interaction network,Bioinformatics,Drug | Journal |
Volume | Issue | ISSN |
7 Suppl 3 | S-3 | 1752-0509 |
Citations | PageRank | References |
6 | 0.40 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minho Lee | 1 | 94 | 6.96 |
| Keunwan Park | 2 | 17 | 1.80 |
| Dongsup Kim | 3 | 320 | 23.11 |