US 12,094,575 B2
Automated detection of boundaries in mass spectrometry data
Daniel Serie, San Mateo, CA (US); and Zhenqin Wu, Palo Alto, CA (US)
Assigned to Venn Biosciences Corporation, South San Francisco, CA (US)
Filed by Venn Biosciences Corporation, South San Francisco, CA (US)
Filed on Nov. 20, 2023, as Appl. No. 18/515,039.
Application 18/515,039 is a division of application No. 16/833,324, filed on Mar. 27, 2020, granted, now 11,869,634.
Claims priority of provisional application 62/826,228, filed on Mar. 29, 2019.
Prior Publication US 2024/0257916 A1, Aug. 1, 2024
Int. Cl. G16B 40/00 (2019.01); G16B 5/00 (2019.01)
CPC G16B 40/00 (2019.02) [G16B 5/00 (2019.02)] 15 Claims
OG exemplary drawing
 
1. A method, comprising:
featurizing data from raw MS data, wherein the raw MS data comprises a plurality of analog samples, each representing mass/charge intensity, the featurizing comprising, for each of the analog samples, the steps of
centering the sample within a retention time window;
discretizing the sample into a sequence of points representing intensity values;
standardizing the intensity values; and
assigning labels to points among the sequence of points corresponding to peak start and peak stop times to produce a labeled sequence of points;
wherein the labeled sequence of points are configured for training a machine learning model to predict an abundance in unseen MS data.