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Preprint.pdf

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🔗 Source: archive.gersteinlab.org
📊 Size: 478 KB
📄 Pages: 11 pages
⬇️ Downloads: 124

Summary

Researchers propose a machine-learning approach to predict essential genes in fungal genomes, using sequence features that correlate with essentiality, rather than relying on homology mapping to known essential genes in model organisms. They identified 14 characteristic sequence features associated with essentiality and used a Bayesian framework to measure their correlation with essentiality in Saccharomyces cerevisiae. A machine learning classifier was trained on known essential genes in S. cerevisiae and applied to the closely related Saccharomyces mikatae, with predictive success assessed through comparison with homology mapping and experimental verification of a subset of predictions.

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Description

Researchers propose a machine-learning approach to predict essential genes in fungal genomes, using sequence features that correlate with essentiality, rather...

Technical Information

  • File Format: PDF
  • File Size: 478 KB
  • Pages: 11
  • Language: EN
  • Total Downloads: 124
  • Last Updated: 6 days ago

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