Article

AI proteomics: from protein identification to virtual cells

AI proteomics: from protein identification to virtual cells

Sun Y, A J, Liu Z, Sun R, Qian L, Payne SH, Bittremieux W, Ralser M, Li C, Chen Y, Dong Z, Perez-Riverol Y, Khan A, Sander C, Aebersold R, Vizcaíno JA, Krieger JR, Yao J, Han W, Zhang L, Zhu Y, Xuan Y, Sun BB, Qiao L, Hermjakob H, Tang H, Gao H, Deng Y, Zhong Q, Chang C, Bandeira N, Li M, E W, Sun S, Yang Y, Omenn GS, Zhang Y, Xu P, Fu Y, Liu X, Overall CM, Wang Y, Deutsch EW, Chen L, Cox J, Demichev V, He F, Huang J, Jin H, Liu C, Li N, Luan Z, Song J, Yu K, Wan W, Wang T, Zhang K, Zhang L, Bell PA, Mann M, Zhang B and Guo T

This review surveys AI across mass-spectrometry proteomics, including peptide identification, quantitative analysis, biomarker discovery and models that connect molecular measurements to cell behaviour.

It directly supports Eliptica’s use of machine learning to turn large proteomic datasets into clinically useful evidence.


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