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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...

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining functionally relevant gene sets for analyzing physiologically novel clinical expression data.

Sevin Turcan1, Douglas E Vetter, Jill L Maron

  • 1Department of Biomedical Engineering, Tufts University, 4 Colby St., Medford, MA 02155, USA. turcans@mskcc.org

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 2, 2010
PubMed
Summary

This study introduces a new method to discover novel gene sets for transcriptomic analysis, even when biological functions are not well-characterized. This approach enhances the sensitivity of analyzing complex diseases like diabetes.

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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:

  • Genomics
  • Bioinformatics
  • Translational Medicine

Background:

  • Gene set analyses are crucial for transcriptomic studies but rely on pre-defined gene sets.
  • Novel physiological problems often lack relevant gene sets in existing databases, potentially biasing results.

Purpose of the Study:

  • To develop a method for mining novel functional gene sets from public expression data for translational research.
  • To address the limitations of existing gene set databases in characterizing novel or poorly understood physiological processes.

Main Methods:

  • Utilized targeted training data from public expression repositories.
  • Defined new criteria for selecting biclusters as candidate gene sets.
  • Validated discovered gene sets on independent clinical datasets across different species and disease states.

Main Results:

  • Discovered novel, uncharacterized gene sets with coherent differential expression in new clinical data.
  • Identified gene sets diagnostic of diabetes in human metabolic data.
  • Demonstrated efficacy across datasets related to neuronal processes and human development, irrespective of annotation completeness.

Conclusions:

  • The developed method efficiently generates relevant gene sets for analyzing data in novel clinical applications.
  • This approach is particularly valuable when existing functional annotation is incomplete or limited.
  • The discovered gene sets show consistent differential expression across diverse biological contexts, suggesting broad applicability.