Plasma Proteomic Signature Predicts Myeloid Neoplasm Risk.

Duc Tran J Scott Beeler Jie Liu Brian Wiley Irenaeus C C Chan Zilan Xin Michael H Kramer Armel L Batchi-Bouyou Xiaoyu Zong Matthew J Walter Giulia E M Petrone Sarantis Chlamydas Francesca Ferraro Stephen T Oh Daniel C Link Ben Busby Yin Cao Kelly L Bolton

Published in: Clinical cancer research : an official journal of the American Association for Cancer Research (2024)

These data highlight the role of immune cell regulation in the progression of CH to MN and the promise of leveraging multi-omic characterization of CH to improve MN risk stratification.

Keyphrases

room temperature
big data
ionic liquid
electronic health record
bone marrow
dendritic cells
acute myeloid leukemia
transition metal
low grade
machine learning
artificial intelligence