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Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

[Link to preprint]

Authors: Amanda Momenzadeh PharmD1,2,3, Simion Kreimer PhD2,3, Dongchuan Guo PhD6, Matthew Ayres2, Daniel Berman MD3,5, Kuang-Yuh Chyu MD3, Prediman K Shah MD3, Dianna Milewicz MD PhD6, Ali Azizzadeh MD, Jesse G. Meyer PhD1,2,3*, Sarah Parker PhD2,3,4*
*Co-Corresponding

1 Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, California, USA
2 Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA
3 Schmidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California, USA
4 Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles California, USA
5 Cedars-Sinai Imaging Department, Cedars Sinai Medical Center, Lost Angeles, California, USA
6 Department of Internal Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, Texas

This repository contains the code used to generate all the figures while hiding the exact deidentified patient numbers.

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