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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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gSELECT: A novel pre-analysis machine-learning library enabling early hypothesis testing and predictive gene

Deniz Caliskan1, Aylin Caliskan1, Thomas Dandekar1

  • 1Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, Würzburg D-97074, Germany.

Computational and Structural Biotechnology Journal
|August 18, 2025
PubMed
Summary
This summary is machine-generated.

gSELECT is a new Python library that evaluates gene sets for classifying biological conditions using gene expression data. It enables hypothesis-driven testing and assesses candidate genes before extensive downstream analyses.

Keywords:
Dimension reductionExplainable AIGene combinationsGenerative AIMachine learningPrioritise genesTranscriptome

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Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Computational Biology

Background:

  • Evaluating gene sets for condition separation is crucial in transcriptomic analysis.
  • Existing workflows often lack direct evaluation of predefined gene sets in classification.
  • This limits assessing literature-derived panels or biological hypotheses.

Purpose of the Study:

  • To develop gSELECT, a Python library for evaluating classification performance of gene sets.
  • To enable assessment of both automatically ranked and user-defined gene sets.
  • To support hypothesis-driven gene set evaluation without data-derived bias.

Main Methods:

  • gSELECT processes .csv or .h5ad expression matrices with group labels.
  • Gene selection methods include mutual information ranking, random sampling, or custom input.
  • Classification uses multilayer perceptrons with Monte Carlo cross-validation and supports train/test splits.

Main Results:

  • gSELECT facilitates hypothesis-driven testing of gene sets.
  • It allows direct evaluation of known or candidate markers.
  • Exhaustive and greedy strategies identify minimal gene combinations for high predictive power.

Conclusions:

  • gSELECT serves as a pre-analysis tool for evaluating dataset separability.
  • It supports early assessment of candidate genes.
  • The library aids in prioritizing genes before resource-intensive downstream analyses.