PharmGKB: the Pharmacogenetics Knowledge Base

By M. Hewett, D. E. Oliver, Daniel L. Rubin, K. L. Easton, Joshua M. Stuart, Russ B. Altman, Teri E. Klein. 2002

436 citations recorded by the source corpus.

Subfields: Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Ben M. Webb
2. Andrej Sali
3. Min-Yi Shen
4. David Eramian
5. Narayanan Eswar
6. Yuan Luo
7. Ursula Pieper
8. Mallur Srivatsan Madhusudhan

In Pharmacology

1. Vijay S. Pande
2. David S. Wishart
3. Mehmet Gönen

Citations in the corpus

• SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, background): “While several databases are available containing vast amounts of chemical data[22-30], when querying these databases for a given drug conflicting information is occasionally retrieved.”
• ChemProt: a disease chemical biology database (2010, neutral): “Drug-target information was collected from DrugBank and PharmGKB.”
• DrugBank: a comprehensive resource for in silico drug discovery and exploration (2005, background): “Ideally, what is needed is something that combines the strengths of, say, PharmGKB, PubChem and Swiss-Prot to *To whom correspondence should be addressed.”
• Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “The first release of ChemProt was in 2011, which collected data from eight public databases, i.e. ChEMBL [238], BindingDB [257], PDSP Ki database [258], DrugBank [244], PharmGKB [259], PubChem bioassay [260], CTD [261] and STITCH [248] and two commercial databases, WOMBAT and WOMBAT-PK [262].”
• Xenobiotic, Bile Acid, and Cholesterol Transporters: Function and Regulation (2010, background): “Likewise, the allele frequencies for the various polymorphisms are not included but can be ascertained from the Hapmap project or PharmGKB databases.”
• Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “focused, such as PharmGKB [17], VARIMED [18] and ClinVar [19].”
• Ontologies in biology: design, applications and future challenges (2004, background): “A further example is PharmGKB , which handles PHARMACOGENETIC informatio”
• MODBASE: a database of annotated comparative protein structure models and associated resources (2005, methodology): “Access through external databases ModBase models in academic and public datasets are also directly accessible from several databases, including the PMP (107), UniProtKB (109), PIR’s iProClass (103), EBI’s InterPro (105), the UCSC Genome Browser (104), PubMed (LinkOut) (110), PharmGKB (106) and SFLD ”
• WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013 (2013, methodology): “We first down-loaded the disease and drug terms from PharmGKB (23) and then used GLAD4U to derive a gene list for each term.”
• Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “Others are more clinically focused, such as PharmGKB [12], VARIMED [13] and ClinVar [14].”
• An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge (2014, methodology): “There were a large variety of annotation sources (see Table 4), including but not limited to: OMIM [50], Uniprot [51], SeattleSeq [52], SNPedia [53], NCBI Clin-Var, PharmGKB [54], Human Gene Mutation Database [55], dbNSFP [56], and in-house annotations.”
• Fonduer: Knowledge Base Construction from Richly Formatted Data (2017, neutral): “Extensive efforts have been made to build large, high-quality knowledge bases (KBs), such as Freebase [5], YAGO [38], IBM Watson [6, 10], PharmGKB [17], and Google Knowledge Graph [37].”