Name
Abstract | ||
|---|---|---|
Drug pharmacokinetics parameters, drug interaction parameters, and pharmacogenetics data have been unevenly collected in different databases and published extensively in the literature. Without appropriate pharmacokinetics ontology and a well annotated pharmacokinetics corpus, it will be difficult to develop text mining tools for pharmacokinetics data collection from the literature and pharmacokinetics data integration from multiple databases.A comprehensive pharmacokinetics ontology was constructed. It can annotate all aspects of in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. It covers all drug metabolism and transportation enzymes. Using our pharmacokinetics ontology, a PK-corpus was constructed to present four classes of pharmacokinetics abstracts: in vivo pharmacokinetics studies, in vivo pharmacogenetic studies, in vivo drug interaction studies, and in vitro drug interaction studies. A novel hierarchical three level annotation scheme was proposed and implemented to tag key terms, drug interaction sentences, and drug interaction pairs. The utility of the pharmacokinetics ontology was demonstrated by annotating three pharmacokinetics studies; and the utility of the PK-corpus was demonstrated by a drug interaction extraction text mining analysis.The pharmacokinetics ontology annotates both in vitro pharmacokinetics experiments and in vivo pharmacokinetics studies. The PK-corpus is a highly valuable resource for the text mining of pharmacokinetics parameters and drug interactions. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1471-2105-14-35 | BMC Bioinformatics |
Keywords | Field | DocType |
bioinformatics,microarrays,algorithms,pharmacokinetics,data mining | Data integration,Ontology,Text mining,Biology,Pharmacokinetics,Bioinformatics | Journal |
Volume | Issue | ISSN |
14 | 1 | 1471-2105 |
Citations | PageRank | References |
17 | 0.60 | 7 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng-Yi Wu | 1 | 32 | 4.58 |
| Shreyas D. Karnik | 2 | 37 | 1.55 |
| Abhinita Subhadarshini | 3 | 37 | 1.55 |
| Zhiping Wang | 4 | 102 | 5.30 |
| Santosh Philips | 5 | 19 | 1.02 |
| Xu Han | 6 | 37 | 1.55 |
| Chienwei Chiang | 7 | 17 | 0.60 |
| Lei Liu | 8 | 17 | 0.60 |
| Malaz Boustani | 9 | 17 | 0.94 |
| Luis Mateus Rocha | 10 | 333 | 36.91 |
| Sara K. Quinney | 11 | 55 | 3.16 |
| David A Flockhart | 12 | 48 | 2.45 |
| Lang Li | 13 | 112 | 17.66 |