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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Lee, Chae Won Kim, Sung Min Sa, Soonok Hong, Myunghee Nam, Sang-Min Han, Hyun Wook |
| Abstract | Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug–gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures. |
| Related Links | https://bmcmedgenomics.biomedcentral.com/counter/pdf/10.1186/s12920-023-01444-8.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17558794 |
| DOI | 10.1186/s12920-023-01444-8 |
| Journal | BMC Medical Genomics |
| Issue Number | 1 |
| Volume Number | 16 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2023-01-30 |
| Access Restriction | Open |
| Subject Keyword | Human Genetics Microarrays Gene Expression Bioinformatics Drug-gene network Network analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics (clinical) Genetics |
| Journal Impact Factor | 2.1/2023 |
| 5-Year Journal Impact Factor | 2.5/2023 |
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