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Results: 7
Number of items: 7
  • Woldegebriel, M., & Derks, E. (2017). Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry. Analytical Chemistry, 89(2), 1212-1221. https://doi.org/10.1021/acs.analchem.6b03678
  • Open Access
    Woldegebriel, M. T. (2017). Innovative methods for data analysis in analytical chemistry using Bayesian statistics and machine learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Woldegebriel, M., Gonsalves, J., van Asten, A., & Vivó-Truyols, G. (2016). Robust Bayesian Algorithm for Targeted Compound Screening in Forensic Toxicology. Analytical Chemistry, 88(4), 2421-2430. https://doi.org/10.1021/acs.analchem.5b04484
  • Woldegebriel, M., Zomer, P., Mol, H. G. J., & Vivó-Truyols, G. (2016). Application of Fragment Ion Information as Further Evidence in Probabilistic Compound Screening Using Bayesian Statistics and Machine Learning: A Leap Toward Automation. Analytical Chemistry, 88(15), 7705-7714. https://doi.org/10.1021/acs.analchem.6b01630
  • Woldegebriel, M., & Vivó-Truyols, G. (2016). A New Bayesian Approach for Estimating the Presence of a Suspected Compound in Routine Screening Analysis. Analytical Chemistry, 88(19), 9843-9849. https://doi.org/10.1021/acs.analchem.6b03026
  • Woldegebriel, M. (2015). Novel Method for Calculating a Nonsubjective Informative Prior for a Bayesian Model in Toxicology Screening: A Theoretical Framework. Analytical Chemistry, 87 (22)(22), 11398-11406. https://doi.org/10.1021/acs.analchem.5b02916
  • Woldegebriel, M., & Vivó-Truyols, G. (2015). Probabilistic model for untargeted peak detection in LC-MS using Bayesian statistics. Analytical Chemistry, 87(14), 7345-7355. https://doi.org/10.1021/acs.analchem.5b01521
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