<p><i>De novo</i> prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization</p>

Shuangxia Ren; Yifeng  Tao; Ke Yu; Yifan Xue; Russell Schwartz; Xinghua Lu

doi:10.7490/f1000research.1118807.1

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Ren S, Tao Y, Yu K et al. De novo prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization [version 1; not peer reviewed]. F1000Research 2021, 10:993 (poster) (https://doi.org/10.7490/f1000research.1118807.1)

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De novo prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization

Shuangxia Ren, Yifeng Tao, Ke Yu, Yifan Xue, Russell Schwartz¹, Xinghua Lu²

Published 30 Sep 2021 (https://doi.org/10.7490/f1000research.1118807.1)

Author Affiliations

¹ Carnegie Mellon University, USA
² University of Pittsburgh, USA

Copyright © 2021 Ren S 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 30 Sep 2021

De novo prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization
[version 1; not peer reviewed]

Shuangxia Ren, Yifeng Tao, Ke Yu, Yifan Xue, Russell Schwartz¹, Xinghua Lu²

Author Affiliations

¹ Carnegie Mellon University, USA
² University of Pittsburgh, USA

Presented at

Pacific Symposium on Biocomputing (PSB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Drug Sensitivity Prediction; Matrix Factorization; Collaborative Filtering; DrugEmbedding; Deep Learning

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De novo prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization

De novo prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization [version 1; not peer reviewed]

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De novo prediction of Cell-Drug sensitivities using deep learning-based graph regularized matrix factorization
[version 1; not peer reviewed]