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Updated: Jun 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

PACdb: a database for cell-based pharmacogenomics.

Eric R Gamazon1, Shiwei Duan, Wei Zhang

  • 1Department of Medicine, The University of Chicago, Chicago, IL 60637, USA.

Pharmacogenetics and Genomics
|March 11, 2010
PubMed
Summary
This summary is machine-generated.

We created the Pharmacogenomics And Cell database (PACdb) to link genetic variants to drug responses. This resource aids in understanding how genetic differences affect cellular sensitivity to anticancer drugs.

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Last Updated: Jun 15, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Genomics
  • Pharmacology
  • Bioinformatics

Background:

  • Understanding genetic influences on drug response is crucial for personalized medicine.
  • Existing databases may not comprehensively link genetic variations, gene expression, and drug sensitivity in cell models.

Purpose of the Study:

  • To develop and present the Pharmacogenomics And Cell database (PACdb).
  • To facilitate the discovery of genetic variants associated with cellular drug response.
  • To support analysis of gene expression and drug sensitivity across diverse populations.

Main Methods:

  • Developed PACdb as a relational database.
  • Integrated data on single nucleotide polymorphisms (SNPs), gene expression, and drug sensitivity.
  • Included analysis of differentially expressed genes between HapMap samples (European and African ancestry).
  • Supported queries for correlations between gene expression and pharmacological phenotypes.

Main Results:

  • PACdb currently includes data for anticancer agents: carboplatin, cisplatin, etoposide, daunorubicin, and cytarabine (Ara-C).
  • The database supports investigation of confounding variables like cell proliferation rates in lymphoblastoid cell lines.
  • Facilitates summary analysis of gene expression differences and gene expression-pharmacological phenotype correlations.

Conclusions:

  • PACdb provides a valuable resource for pharmacogenomic and pharmacogenetic research.
  • Future updates will expand drug coverage and include new datasets like microRNA levels.
  • Integration with PharmGKB will enhance future pharmacogenetic discoveries.