SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations-Reference-Cited by-同舟云学术

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SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations

Published:2024-07-30 Issue:1 Volume:25 Page:
ISSN:1471-2105
Container-title:BMC Bioinformatics
language:en
Short-container-title:BMC Bioinformatics

Author:

Tang Yi-Ching,Li Rongbin,Tang Jing,Zheng W. Jim,Jiang Xiaoqian

Funder

Gulf Coast Consortium for Innovative Drug Discovery and Development

Academy of Finland

Christopher Sarofim Family Professionship, UT Starts award, UTHealth startup

National Institutes of Health

National Institutes of Health, United States

National Institutes of Health,United States

National Science Foundation

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s12859-024-05873-9.pdf

Reference52 articles.

1. Foucquier J, Guedj M. Analysis of drug combinations: current methodological landscape. Pharmacol Res Perspect. 2015;3(3):e00149.

2. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med. 2001;7(5):201–4.

3. Cohen RL, Settleman J. From cancer genomics to precision oncology—tissue’s still an issue. Cell. 2014;157(7):1509–14.

4. Garcia-Alonso L, Iorio F, Matchan A, Fonseca N, Jaaks P, Peat G, et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 2018;78(3):769–80.

5. Hao Y, Quinnies K, Realubit R, Karan C, Tatonetti NP. Tissue-specific analysis of pharmacological pathways. CPT Pharmacomet Syst Pharmacol. 2018;7(7):453–63.

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