REAP Workshop Fall 2026

Yu (Charlie) Sun, Graduate Student in the Yu Lab, attended the Machine Learning for Mass Spectrometry Data Analysis short course, hosted by the American Society for Mass Spectrometry on May 30–31, 2026, in San Diego, CA as a recipient of the Weill Institute REAP award. Objective The post-workshop presentation will demonstrate how core machine learning concepts introduced in the workshop can be applied to a real mass spectrometry data analysis problem. The goal is to reinforce data literacy, highlight best practices, and show how to critically interpret machine learning results rather than to develop advanced models. Target Audience This presentation is intended for mass spectrometry researchers seeking a deeper understanding of fundamental machine learning concepts, commonly used algorithms, and typical pitfalls. Emphasis will be placed on developing the ability to critically evaluate machine learning results in the scientific literature and to apply these approaches responsibly and appropriately in their own mass spectrometry studies. Presentation Content The presentation will focus on a concrete, well-defined mass spectrometry use case (e.g., peptide identification confidence, sample classification, or feature selection in proteomics). It will include: Problem definition – the biological or analytical question and why machine learning is appropriate.Data overview – structure of the mass spectrometry data, preprocessing steps, and common challenges (missing values, batch effects, dimensionality).Machine learning approach – a simple, standard method covered in the workshop (e.g., regression, classification, or clustering), with emphasis on model assumptions and parameter choices.Results and interpretation – performance metrics, visualization of outputs, and biological relevance.Limitations – potential overfitting, data leakage, and when expert consultation would be necessary. Outcome By the end of the presentation, attendees should understand how basic machine learning tools integrate into mass spectrometry data analysis workflows and feel more confident interpreting and applying these methods in practice.