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Updated: May 10, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Bioinformatic challenges for pharmacogenomic study: tools for genomic data analysis.

Mariamena Arbitrio¹, Marianna Milano², Maria Lucibello¹

¹Institute for Biomedical Research and Innovation, National Research Council, Catanzaro, Italy.

Frontiers in Pharmacology

|April 28, 2025

Summary

This summary is machine-generated.

Personalized medicine uses genetic information to tailor treatments, advancing drug efficacy and safety. This review proposes bioinformatics guidelines to simplify complex pharmacogenomic data analysis for broader clinical application.

Keywords:

bioinformatics biological pathways genomic data analysis network analysis, pathway enrichment analysis pharmacogenomics

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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

Genomics and Bioinformatics
Pharmacogenomics (PGx)
Personalized Medicine

Background:

Human genome sequencing initiated personalized medicine, moving beyond one-size-fits-all treatments.
Next-Generation Sequencing (NGS) generates large datasets for tailored therapies.
Pharmacogenomics (PGx) identifies genetic influences on drug response and toxicity, discovering biomarkers.

Purpose of the Study:

To propose a guideline for simplifying complex DMET PGx data analysis.
To enhance accessibility of high-performance bioinformatics for PGx applications, including for non-specialists.
To demonstrate how bioinformatics tools facilitate integrative analysis for personalized therapeutic strategies.

Main Methods:

Review of experience with DMET microarray platform in PGx studies.
Development of a guideline combining machine learning, statistical, and network-based approaches.
Description of bioinformatic tool application for comprehensive integrative analysis.

Main Results:

A proposed guideline to simplify and improve understanding of complex DMET PGx data analysis.
Demonstration of bioinformatic tools for translating genetic insights into personalized therapies.
Increased accessibility to advanced bioinformatics for broader PGx applications.

Conclusions:

Integrating diverse bioinformatics approaches simplifies complex PGx data analysis.
Bioinformatic tools are crucial for translating genomic findings into personalized medicine.
Guidelines can foster wider adoption of advanced PGx analysis in clinical practice.