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Metabolomics Outlier Detector.pdf

Poster-Abstracts-6-22-16.pdf
Preview of Metabolomics Outlier Detector
🔗 Source: metabolomics2016.org
📊 Size: 1.99 MB
👤 Author: Jason Stinson
⬇️ Downloads: 340

Summary

1. MoOD, a novel metabolomics outlier detector for R-Shiny to minimize technical errors in sample preparation and measurement.
2. AddClique, a network-based algorithm for identifying adducts in LC/MS metabolomics to aid in signal interpretation.
3. Quantification of bioactive N-acylethanolamines in human plasma using LC-MS/MS.
4. Metabolic changes in prefrontal cortex of humans, chimpanzees, and macaques during postnatal development, showing substantial changes with age and species-specific differences.
5. Assessing antimicrobial penetration into Pseudomonas aeruginosa biofilms using ToF-SIMS, highlighting the reduced susceptibility of biofilm cells to antimicrobials.

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Description

1. MoOD, a novel metabolomics outlier detector for R-Shiny to minimize technical errors in sample preparation and measurement.
2.

Technical Information

  • File Format: PDF
  • File Size: 1.99 MB
  • Pages: 489
  • Language: EN
  • Author: Jason Stinson
  • Total Downloads: 340
  • Last Updated: 5 days ago

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This PDF document about Metabolomics Outlier Detector provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Metabolomics Outlier Detector.

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