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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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Factors Affecting Solubility04:01

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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
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Transcription Elongation Factors02:35

Transcription Elongation Factors

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Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
The transcription elongation is regulated via pausing of RNA polymerase on several occasions during transcription. In bacteria, these halts are necessary because the transcription of DNA into mRNA is coupled to the translation of that mRNA...
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Factors Affecting Drug Distribution: Miscellaneous Factors01:19

Factors Affecting Drug Distribution: Miscellaneous Factors

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Drug distribution in the human body is a complex process influenced by various individual factors, including age, pregnancy, obesity, diet, body water composition, pH levels, and specific disease conditions.
Age plays a significant role due to differences in body composition among different age groups. Infants, for instance, have a higher proportion of total body water and lower albumin levels, a protein that binds drugs in the bloodstream. This unique composition in infants enhances the...
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Electrolytes: van't Hoff Factor03:08

Electrolytes: van't Hoff Factor

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Colligative Properties of Electrolytes
The colligative properties of a solution depend only on the number, not on the identity, of solute species dissolved. The concentration terms in the equations for various colligative properties (freezing point depression, boiling point elevation, osmotic pressure) pertain to all solute species present in the solution. Nonelectrolytes dissolve physically without dissociation or any other accompanying process. Each molecule that dissolves yields one...
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Factors Affecting Protein-Drug Binding: Drug-Related Factors01:18

Factors Affecting Protein-Drug Binding: Drug-Related Factors

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Drug binding to proteins is a complex phenomenon influenced by various drug-related factors, each playing a significant role in the interaction between drugs and proteins within the body.
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In...
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Rapid Formation and Testing of Self-expanding NiTi Frames with a Small Form Factor Suitable for Minimally Invasive Implants
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Expandable factor analysis.

Sanvesh Srivastava1, Barbara E Engelhardt2, David B Dunson3

  • 1Department of Statistics and Actuarial Science, University of Iowa, 241 Schaeffer Hall, 20 East Washington Street, Iowa City, Iowa 52242, U.S.A.sanvesh-srivastava@uiowa.edu.

Biometrika
|February 13, 2018
PubMed
Summary
This summary is machine-generated.

Expandable factor analysis offers scalable Bayesian inference for multivariate data, improving accuracy in identifying underlying factors. This method enhances discovery in complex datasets, outperforming existing approaches.

Keywords:
Expectation-maximization algorithmFactor analysisShrinkage priorSparsityVariable selection

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Area of Science:

  • Statistics
  • Bioinformatics
  • Computational Biology

Background:

  • Bayesian sparse factor models are valuable for multivariate data dependence but face computational challenges with large datasets.
  • Scalability issues hinder the application of traditional factor models in high-dimensional and large-sample scenarios.

Purpose of the Study:

  • To develop a scalable Bayesian factor analysis method for handling unknown numbers of factors.
  • To introduce a continuous shrinkage prior for efficient estimation of low-rank and sparse loadings.
  • To provide a method that accommodates uncertainty in factor numbers and offers improved performance.

Main Methods:

  • Proposing expandable factor analysis with a continuous shrinkage prior for maximum a posteriori estimation.
  • Developing an estimation algorithm that integrates uncertainty in the number of factors.
  • Introducing an information criterion for selecting prior hyperparameters.

Main Results:

  • The proposed expandable factor analysis demonstrates superior false discovery and true positive rates compared to competitors in simulations.
  • The method effectively handles scenarios with an unknown number of factors.
  • Application to a mouse gene expression study of aging shows superior performance over four competing methods.

Conclusions:

  • Expandable factor analysis provides a scalable and accurate approach for Bayesian factor modeling.
  • The method offers significant advantages in analyzing complex biological data, such as gene expression.
  • This approach enhances the ability to characterize dependence in large-scale multivariate datasets.