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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Transcription Factors02:16

Transcription Factors

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General Transcription Factors01:30

General Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Master Transcription Regulators02:23

Master Transcription Regulators

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Master Transcription Regulators02:23

Master Transcription Regulators

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Combinatorial Gene Control02:33

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.
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...
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Matrix Factorization for Transcriptional Regulatory Network Inference.

Michael F Ochs1, Elana J Fertig1

  • 1School of Medicine, Johns Hopkins University, Baltimore, MD 21205.

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology Proceedings. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
|November 4, 2014
PubMed
Summary
This summary is machine-generated.

Inferring transcriptional regulatory networks (TRNs) is key to understanding biology. Non-negative matrix factorization offers a powerful approach for TRN analysis, especially in complex multicellular organisms.

Keywords:
Bayesian statisticsMatrix factorizationNMFTranscriptional Regulatory Network

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Area of Science:

  • Systems biology
  • Genomics
  • Bioinformatics

Background:

  • Transcriptional Regulatory Networks (TRNs) are crucial for understanding biological mechanisms in development and disease.
  • Current TRN inference methods often struggle with the complexity of multicellular organisms due to multiple gene regulation and reuse.
  • Accurate TRN inference is vital for advancing our understanding of biological systems.

Purpose of the Study:

  • To review non-negative matrix factorization (NMF) techniques for Transcriptional Regulatory Network (TRN) inference.
  • To highlight the suitability of NMF for analyzing complex regulatory interactions in multicellular organisms.
  • To provide insights into the application of NMF for TRN analysis.

Main Methods:

  • Review of existing literature on TRN inference techniques.
  • Focus on Non-negative Matrix Factorization (NMF) methods.
  • Analysis of NMF's application in estimating regulatory interactions from biochemical data.

Main Results:

  • NMF techniques are well-suited for identifying non-orthogonal patterns in biological data.
  • NMF demonstrates potential for improving TRN inference accuracy in complex biological systems.
  • The review consolidates understanding of NMF's role in TRN analysis.

Conclusions:

  • Non-negative matrix factorization presents a robust methodology for Transcriptional Regulatory Network inference.
  • NMF addresses challenges posed by multiple gene regulation and reuse in multicellular organisms.
  • Further application of NMF is recommended for advancing TRN analysis in complex biological contexts.