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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
RNA Splicing01:32

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: May 18, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Transcriptome classification reveals molecular subtypes in psoriasis.

Chrysanthi Ainali1, Najl Valeyev, Gayathri Perera

  • 1Centre for Bioinformatics, Department of Informatics, School of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UK.

BMC Genomics
|September 14, 2012
PubMed
Summary
This summary is machine-generated.

Psoriasis gene expression analysis reveals distinct molecular subtypes within plaque psoriasis. These findings may guide personalized treatment strategies by identifying subgroups responsive to specific pathway therapies.

Related Experiment Videos

Last Updated: May 18, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Dermatology
  • Immunology
  • Bioinformatics

Background:

  • Psoriasis is an immune-mediated condition with elevated pro-inflammatory cytokines.
  • Aberrant keratinocyte proliferation and differentiation are hallmarks of psoriasis.
  • Molecular subtypes of psoriasis remain poorly defined, impacting prognosis and treatment.

Purpose of the Study:

  • To develop a patient stratification pipeline using gene expression analysis.
  • To identify molecular subtypes within the plaque psoriasis phenotype.
  • To uncover gene expression signatures differentiating these subtypes.

Main Methods:

  • Comprehensive gene expression analysis of paired lesional and non-lesional psoriatic tissue.
  • Utilized ensembles of decision tree predictors for sample clustering.
  • Applied a multi-stage procedure across multiple psoriasis studies.

Main Results:

  • Distinct molecular subgroups were identified within plaque psoriasis.
  • Gene expression patterns revealed significant differences between subgroups.
  • One subgroup showed enrichment for TGFb and ErbB signaling pathways.

Conclusions:

  • Classification of gene expression patterns identified novel psoriasis subtypes.
  • Specific pathway enrichment suggests potential targeted therapy approaches.
  • Ensemble decision trees are valuable for defining molecular subtypes in seemingly homogenous clinical groups.