<p>Predicting adverse cardiovascular events for tyrosine kinase inhibitors from molecular features</p>

Anders Dohlman; Shadia Zaman; Qiaonan Duan; Zichen Wang; Ravi Iyengar; Darrell Abernethy; Avi Ma'ayan

doi:10.7490/f1000research.1113434.1

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Dohlman A, Zaman S, Duan Q et al. Predicting adverse cardiovascular events for tyrosine kinase inhibitors from molecular features [version 1; not peer reviewed]. F1000Research 2016, 5:2719 (poster) (https://doi.org/10.7490/f1000research.1113434.1)

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Predicting adverse cardiovascular events for tyrosine kinase inhibitors from molecular features

Anders Dohlman, Shadia Zaman, Qiaonan Duan, Zichen Wang, Ravi Iyengar, Darrell Abernethy, Avi Ma'ayan¹

Published 21 Nov 2016 (https://doi.org/10.7490/f1000research.1113434.1)

Author Affiliations

¹ Icahn School of Medicine at Mount Sinai, USA

Copyright © 2016 Dohlman A et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 21 Nov 2016

Predicting adverse cardiovascular events for tyrosine kinase inhibitors from molecular features
[version 1; not peer reviewed]

Anders Dohlman, Shadia Zaman, Qiaonan Duan, Zichen Wang, Ravi Iyengar, Darrell Abernethy, Avi Ma'ayan¹

Author Affiliations

¹ Icahn School of Medicine at Mount Sinai, USA

Presented at

BD2K All Hands Meeting 2016

Abstract

Competing Interests

No competing interests were disclosed

Keywords

BD2K, LINCS, DCIC, side effects predictions, machine learning, TKI, toxicity prediction

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