Agree that this is not written with the rigor of a journal paper. Our intention was to communicate the simple wins we've had employing deep learning in comparis

Agree that this is not written with the rigor of a journal paper. Our intention was to communicate the simple wins we've had employing deep learning in comparison to the tools of the prior generation. XCMS is the most widely used library: http://www.bioconductor.org/packages/release/bioc/html/xcms.html http://www.bioconductor.org/packages/release/bioc/html/xcms..... It requires very painful parameter tuning. Internally, we had also built our own custom targeted analysis. In the targeted pipeline, we had to pre-specify "acetaminophen, shikimate, chorismate...". After building this deep learning workflow, we have exclusively switched over to it: no chemicals pre-specified, no parameter tuning. With about 185 engineered yeast that need analysis, with replicates, feeding conditions, and controls, these simplifications have been helpful. We are getting easy wins over microbial data. Human data is noisier and we're testing over that now. More later. If you have microbial data, or have used XCMS in the past and would like to compare, happy to chat. email me at saurabh@20n.