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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

Incorporating prior knowledge into Gene Network Study.

Zixing Wang1, Wenlong Xu, F Anthony San Lucas

  • 1Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Department of Epidemiology, University of Texas MD Anderson Center and University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030, USA.

Bioinformatics (Oxford, England)
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

We developed a new method, prior Lasso (pLasso), to reconstruct gene networks using pathway information. This approach improves the identification of genes associated with complex diseases, outperforming traditional methods in simulations and cancer patient data analysis.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Identifying genes that jointly influence biological responses is crucial in genomic research.
  • Integrating existing biological pathway knowledge with large-scale genomic data for gene network reconstruction is challenging.
  • Pathway information complements microarray gene expression data for studying complex phenotypes.

Purpose of the Study:

  • To develop and apply a novel Bayesian Lasso method (pLasso) for enhanced gene network reconstruction.
  • To effectively integrate prior biological pathway knowledge into gene expression data analysis.
  • To improve the statistical power and interpretability of gene network studies.

Main Methods:

  • Developed a Bayesian Lasso method named prior Lasso (pLasso).
  • Partitioned gene edges into those present in known pathways and those without prior information.
  • Assigned different prior distributions using a modified Bayesian information criterion incorporating pathway and network structure knowledge.

Main Results:

  • Simulation studies demonstrated pLasso's superior performance in recovering underlying gene networks compared to traditional Lasso.
  • Applied pLasso to microarray gene expression datasets using Pathway Commons and KEGG pathway information.
  • Successfully identified network hub genes linked to clinical outcomes in cancer patients.

Conclusions:

  • The prior Lasso (pLasso) method effectively reconstructs gene networks by integrating prior pathway knowledge.
  • pLasso enhances the identification of biologically relevant gene networks and associated biomarkers.
  • This approach holds promise for advancing our understanding of complex diseases and identifying therapeutic targets.