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Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Related Experiment Video

Updated: May 22, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Relation mining experiments in the pharmacogenomics domain.

Fabio Rinaldi1, Gerold Schneider, Simon Clematide

  • 1Institute of Computational Linguistics, University of Zurich, Binzmühlestrasse 14, 8050 Zürich, Switzerland. fabio.rinaldi@uzh.ch

Journal of Biomedical Informatics
|May 15, 2012
PubMed
Summary
This summary is machine-generated.

The PharmGKB database can serve as a gold standard for evaluating text mining tools used in pharmacogenetics research. This aids in advancing personalized medicine by analyzing gene-drug interactions.

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Last Updated: May 22, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

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Published on: December 11, 2016

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Biomedical research
  • Pharmacology
  • Bioinformatics

Background:

  • Gene-disease-drug interactions are crucial for personalized medicine and the pharmaceutical industry.
  • The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) curates this information from literature.
  • Advanced text mining tools are needed to manage the growing volume of research data.

Purpose of the Study:

  • To explore the utility of the PharmGKB database as a gold standard for evaluating text mining tools.
  • To assess the feasibility and challenges of using PharmGKB for this evaluation task.

Main Methods:

  • Utilizing the existing PharmGKB database for evaluation.
  • Drawing parallels with the use of protein-protein interaction databases in BioCreative tasks.

Main Results:

  • PharmGKB's potential as a gold standard for text mining tool evaluation was discussed.
  • Considerations and preliminary results on the task's feasibility and difficulty were presented.

Conclusions:

  • The PharmGKB database offers a viable resource for evaluating text mining tools in pharmacogenetics.
  • This evaluation framework supports the development of better tools for personalized medicine research.