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Related Concept Videos

What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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...
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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics.

Carlos M Carvalho1, Jeffrey Chang, Joseph E Lucas

  • 1Assistant Professor of Econometrics and Statistics, The University of Chicago, Graduate School of Business, Chicago, IL 60637, carlos.carvalho@chicagogsb.edu.

Journal of the American Statistical Association
|January 11, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces novel sparse latent factor and regression models for analyzing gene expression data in breast cancer. These methods help characterize pathway heterogeneity and link gene patterns to clinical biomarkers.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Molecular profiling generates complex gene expression data.
  • Understanding biological pathways is crucial for disease research.
  • Existing models may not fully capture complex expression patterns.

Purpose of the Study:

  • To develop and apply sparse latent factor and regression models for microarray gene expression data.
  • To investigate breast cancer heterogeneity and oncogenic pathways.
  • To link gene expression patterns to clinical biomarkers.

Main Methods:

  • Utilized sparse latent factor and regression models.
  • Employed sparsity modeling of multivariate regression, ANOVA, and latent factor models.
  • Incorporated hierarchical sparsity priors for dimension reduction and scalability.
  • Applied stochastic simulation and evolutionary stochastic search for model fitting.

Main Results:

  • Demonstrated the ability to decompose sparse factor models into pathway subcomponents.
  • Characterized heterogeneity in breast cancer related to oncogenic pathways.
  • Identified links between gene expression patterns and clinical biomarkers.
  • Showcased the models' capability to handle complex non-Gaussian expression patterns.

Conclusions:

  • Sparse latent factor and regression models offer a powerful approach for analyzing complex gene expression data.
  • The methodology effectively characterizes pathway structure and clinical correlations in breast cancer.
  • The developed computational tools are freely available for broader application.