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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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
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...

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Related Experiment Video

Updated: May 11, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Multiple suboptimal solutions for prediction rules in gene expression data.

Osamu Komori1, Mari Pritchard, Shinto Eguchi

  • 1The Institute of Statistical Mathematics, Midori-cho, Tachikawa, Tokyo 190-8562, Japan. komori@ism.ac.jp

Computational and Mathematical Methods in Medicine
|May 11, 2013
PubMed
Summary
This summary is machine-generated.

Extracting informative genes from microarray data is challenging due to high dimensionality and gene correlations. Current methods struggle to isolate truly predictive gene sets, indicating an ill-posed problem in statistical analysis.

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Related Experiment Videos

Last Updated: May 11, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Published on: March 1, 2024

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Microarray gene expression analysis faces challenges due to the imbalance between the number of observed genes and subjects.
  • Predicting phenotypes from gene expression data requires effective feature extraction and pattern recognition.

Purpose of the Study:

  • To investigate the mathematical and statistical challenges in analyzing microarray gene expression data.
  • To reanalyze published data to identify alternative gene sets for phenotype prediction.
  • To understand the reasons behind the difficulty in extracting informative genes using current statistical machine learning approaches.

Main Methods:

  • Reanalysis of published microarray gene expression datasets.
  • Focus on pattern recognition for phenotype prediction.
  • Analysis of mutual coherence (Pearson correlations) between genes.

Main Results:

  • Multiple gene sets with comparable predictive performance were detected.
  • It is currently not feasible to isolate a single set of highly informative genes with high performance across all observed genes.
  • The difficulty in finding informative genes is linked to the mutual coherence of gene expression data.

Conclusions:

  • The problem of identifying informative genes in high-dimensional microarray data is ill-posed.
  • High mutual coherence among genes significantly contributes to the challenges in gene set selection.
  • Further research into statistical machine learning methods is needed to address these inherent data complexities.