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Generating Proteomic Big Data for Precision Medicine
Author(s) -
Yue Liang,
Zhang Fangfei,
Sun Rui,
Sun Yaoting,
Yuan Chunhui,
Zhu Yi,
Guo Tiannan
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900358
Subject(s) - proteomics , proteome , big data , computer science , data science , sample (material) , data acquisition , software deployment , medical research , precision medicine , computational biology , artificial intelligence , data mining , bioinformatics , medicine , biology , chromatography , chemistry , pathology , software engineering , biochemistry , gene , operating system
Abstract Here, the authors reason that the complexity of medical problems and proteome science might be tackled effectively with deep learning (DL) technology. However, deployment of DL for proteomics data requires the acquisition of data sets from a large number of samples. Based on the success of DL in medical imaging classification, proteome data from thousands of samples are arguably the minimal input for DL. Contemporary proteomics is turning high‐throughput thanks to the rapid progresses of sample preparation and liquid chromatography mass spectrometry methods. In particular, data‐independent acquisition now enables the generation of hundreds to thousands of quantitative proteome maps from clinical specimens in clinical cohorts with only limited sample amounts in clinical cohorts. Upheavals in the design of large‐scale clinical proteomics studies might be required to generate proteomic big data and deploy DL to tackle complex medical problems.