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A Clinically Applicable 24-Protein Model for Classifying Risk Subgroups in Pancreatic Ductal Adenocarcinomas using Multiple Reaction Monitoring-Mass Spectrometry.

Minsoo SonHongbeom KimDo Hyun HanYoseop KimIksoo HuhYoungmin HanSeung-Mo HongWooil KwonHaeryoung KimJin-Young JangYoungsoo Kim
Published in: Clinical cancer research : an official journal of the American Association for Cancer Research (2021)
We proposed PDAC risk subgroups and developed a classification model that may potentially be useful for routine clinical implementations, at the individual level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.See related commentary by Thakur and Singh, p. 3272.
Keyphrases
  • mass spectrometry
  • clinical practice
  • machine learning
  • deep learning
  • liquid chromatography
  • small molecule
  • capillary electrophoresis
  • high performance liquid chromatography
  • breast cancer risk