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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: Jun 4, 2025

Development of Compendium for Esophageal Squamous Cell Carcinoma

Development of Compendium for Esophageal Squamous Cell Carcinoma

Published on: April 12, 2024

Gene expression ranking change based single sample pre-disease state detection.

Zhenshen Bao¹, Xianbin Li², Peng Xu³

¹School of Information Engineering, Taizhou University, Taizhou, Jiangsu, China.

Frontiers in Genetics

|December 19, 2024

Summary

This summary is machine-generated.

Detecting the pre-disease state is crucial for prevention. A new method, S-PCR (single-sample pre-disease state identification based on the change in gene expression ranking), rapidly identifies this critical transition point using gene expression changes.

Keywords:

personalized disease diagnosis pre-disease state ranking change single sample state transition

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Early disease detection is vital for prevention.
Identifying the pre-disease state is challenging with single samples.
Gene expression regulation shifts drive biological state transitions.

Purpose of the Study:

To develop a rapid, single-sample method for pre-disease state identification.
To address the challenge of detecting critical biological transitions.
To leverage gene expression ranking for early disease detection.

Main Methods:

Proposed a novel method: S-PCR (single-sample pre-disease state identification based on the change in gene expression ranking).
Utilized changes in gene expression ranking to reflect coordinated gene shifts.
Developed a model-free computational approach.

Main Results:

Successfully identified pre-disease states in simulated and five real-world biological datasets.
Functional analysis of identified genes confirmed the method's effectiveness.
S-PCR demonstrated significantly improved time efficiency compared to existing methods.

Conclusions:

S-PCR is an effective computational approach for identifying pre-disease states.
The method shows high potential for clinical applications in personalized disease diagnosis.
Rapid and accurate pre-disease detection is achievable with S-PCR.