Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.09.242727v1?rss=1 Authors: Gloaguen, Y., Kirwan, J., Beule, D. Abstract: Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS which uses machine learning to replace peak curation by human experts. We show how to integrate our open source module into different LC-MS analysis workflows and quantify its performance. NeatMS is designed to be suitable for large scale studies and improves the robustness of the final peak list. Copy rights belong to original authors. Visit the link for more info