MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein–protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using ne...

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Bibliographic Details
Main Author:	Emily Hashimoto-Roth (9564009) (author)
Other Authors:	Diane Forget (2007433) (author), Vanessa P. Gaspar (16511018) (author), Steffany A. L. Bennett (9564015) (author), Marie-Soleil Gauthier (6863540) (author), Benoit Coulombe (8211) (author), Mathieu Lavallée-Adam (1343481) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Medicine Molecular Biology Physiology Immunology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tandem mass spectrometry novel machine learning effectively detect false antibody nonspecific binding false positive identifications existing modeling algorithms machine learning approach leverages negative controls magpie significantly outperformed negative controls used human plasma samples positive interactions often used human plasma computational modeling based approach approach provides unprecedented ability proteins cannot predicted ppis ppis detected ppis ). introduce magpie identifying ppis identified known first constructed execution without biological processes better understanding 77 %.
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MAGPIE: A Machine Learning Approach to Decipher Protein–Protein Interactions in Human Plasma

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