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Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Related Experiment Video

Updated: Sep 11, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

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diffMIN: Reconstructing Sample-Specific Differential Gene Regulatory Networks Based on Mutual Information.

Songwei Ai, Yiwei Zhou, Wenkui Zou

IEEE Transactions on Computational Biology and Bioinformatics

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces diffMIN, a novel method for creating sample-specific gene regulatory networks. diffMIN enhances cell clustering and identifies key genes for disease prognosis, outperforming existing approaches.

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Area of Science:

Genomics and Systems Biology
Computational Biology
Bioinformatics

Background:

Gene regulatory mechanisms are fundamental to biological systems.
Sample-specific networks (SSNs) offer precise insights into biological processes.
Current methods for SSN inference are limited to individual samples.

Purpose of the Study:

To develop a novel computational method for reconstructing sample-specific differential gene regulatory networks.
To evaluate the performance of the new method in cell clustering and gene identification.
To apply the method to analyze cardiovascular and cancer datasets.

Main Methods:

Proposed diffMIN (differential Mutual Information) for sample-specific differential gene regulatory network reconstruction.
Utilized mutual information for network inference.
Applied diffMIN to single-cell RNA sequencing datasets, including transverse aortic constriction (TAC) and The Cancer Genome Atlas (TCGA) breast cancer data.

Main Results:

diffMIN demonstrated superior performance in cell clustering compared to existing SSN algorithms.
Analysis of TAC data revealed monotonic changes in regulatory networks during heart failure progression.
Application to breast cancer data identified key genes correlated with patient prognosis, even without significant differential expression.

Conclusions:

diffMIN is a widely applicable method for constructing sample-specific differential networks.
The method excels in cell clustering, identifying crucial genes, investigating biological processes, and aiding disease diagnosis.
diffMIN provides a powerful tool for personalized medicine and understanding complex diseases.