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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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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Combinatorial Gene Control02:33

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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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Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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What is Gene Expression?01:42

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Overview
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Related Experiment Video

Updated: Jun 10, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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GAEM: Genetic Algorithm based Expectation-Maximization for inferring Gene Regulatory Networks from incomplete data.

Parisa Niloofar1, Rosa Aghdam2, Changiz Eslahchi3

  • 1Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark.

Computers in Biology and Medicine
|October 19, 2024
PubMed
Summary

The GAEM algorithm effectively infers Gene Regulatory Network (GRN) structures from incomplete gene expression data by iteratively updating missing values and the GRN. This approach outperforms traditional methods, especially for smaller networks.

Keywords:
Bayesian networkConditional Mutual InformationExpectation-MaximizationGene Regulatory NetworkGenetic algorithmMissing values

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Inferring Gene Regulatory Network (GRN) structures from incomplete gene expression data is a significant challenge in bioinformatics.
  • Existing methods like the Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI) struggle with missing data, necessitating imputation techniques.

Purpose of the Study:

  • To present the GAEM algorithm, a novel method for inferring GRN structures from incomplete gene expression datasets.
  • To address the limitations of traditional imputation-then-GRN-inference approaches by iteratively updating both missing values and the GRN structure.

Main Methods:

  • The GAEM algorithm combines a Genetic Algorithm and Expectation-Maximization in an iterative approach.
  • It learns GRN structure by repeatedly updating imputed values based on the inferred GRN until convergence.

Main Results:

  • GAEM was evaluated under various missingness percentages (5%, 15%, 40%) and mechanisms.
  • Results on the DREAM3 dataset indicate GAEM is a reliable method, outperforming traditional approaches, particularly for smaller network sizes.

Conclusions:

  • The GAEM algorithm offers a robust solution for GRN inference from incomplete gene expression data.
  • The GAEM R package is available, facilitating its application in bioinformatics research.