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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: May 29, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Applying AI/ML for Analyzing Gene Expression Patterns.

Zeeshan Ahmed^1,2

¹Department of Medicine, Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA. zahmed@ifh.rutgers.edu.

Methods in Molecular Biology (Clifton, N.J.)

|February 3, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) can advance genomics by analyzing complex RNA-seq data to discover biomarkers and predict diseases. Our FAIR solutions offer accessible tools for personalized medicine and public health.

Keywords:

Artificial intelligence Expression Gene Patterns Precision medicine

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Area of Science:

Genomics and Bioinformatics
Artificial Intelligence in Medicine
Computational Biology

Background:

Genomics and RNA sequencing generate vast, complex datasets requiring advanced analytical methods.
Current AI and ML progress in genomics lags behind other fields due to data handling and expertise challenges.
Discovering novel biomarkers and predicting diseases necessitates sophisticated analysis of gene expression data.

Purpose of the Study:

To highlight the potential of transcriptomics and RNA-seq for biomarker discovery and disease prediction.
To discuss the challenges and opportunities in applying AI/ML to genomic data analysis.
To introduce Findable, Accessible, Intelligent, and Reproducible (FAIR) solutions for biomarker discovery and disease prediction.

Main Methods:

Exploration of high-volume sequence data and gene expression patterns using bioinformatics tools.
Implementation of AI/ML approaches to identify disease-specific patterns in genomic data.
Development and application of FAIR data solutions for users with varying computational backgrounds.

Main Results:

Identification of significantly expressed and enriched genes through bioinformatics analysis.
Observation of disease-specific patterns using AI/ML techniques.
Development of user-friendly, reproducible AI/ML applications for biomarker discovery.

Conclusions:

AI/ML, particularly with RNA-seq data, holds significant potential for personalized diagnostics and treatments.
FAIR solutions aim to bridge the gap in AI/ML adoption within scientific and clinical settings.
Widespread application of AI/ML in genomics can advance public health through personalized interventions and novel therapeutic targets.