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  Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

Prasse, P., Iversen, P., Lienhard, M., Thedinga, K., Bauer, C., Herwig, R., et al. (2022). Matching anticancer compounds and tumor cell lines by neural networks with ranking loss. NAR: genomics and bioinformatics, 4(1): lqab128. doi:10.1093/nargab/lqab128.

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Prasse et al_2022.pdf (Publisher version), 964KB
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Prasse , Paul , Author
Iversen , Pascal , Author
Lienhard, Matthias1, Author              
Thedinga, Kristina1, Author              
Bauer, Chris, Author
Herwig, Ralf1, Author              
Scheffer, Tobias, Author
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1Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_2385701              

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 Abstract: Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

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Language(s): eng - English
 Dates: 2021-12-292022-01-14
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/nargab/lqab128
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Title: NAR: genomics and bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 4 (1) Sequence Number: lqab128 Start / End Page: - Identifier: ISSN: 2631-9268
CoNE: https://pure.mpg.de/cone/journals/resource/2631-9268