The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 100 of 56543
Back to Result List

Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction

  • Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, weLarge-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Paul PrasseORCiDGND, Pascal IversenORCiD, Matthias LienhardORCiDGND, Kristina Thedinga, Ralf HerwigORCiD, Tobias SchefferORCiD
DOI:https://doi.org/10.3390/cancers14163950
ISSN:2072-6694
Title of parent work (English):MDPI
Publisher:MDPI
Place of publishing:Basel, Schweiz
Publication type:Article
Language:English
Date of first publication:2022/08/16
Publication year:2022
Release date:2023/01/30
Tag:anti-cancer drugs; deep neural networks; drug-sensitivity prediction
Volume:14
Article number:3950
Print run:16
Number of pages:14
First page:1
Last Page:14
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Extern / Extern
DDC classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer review:Referiert
Grantor:Publikationsfonds der Universität Potsdam
Publishing method:Open Access / Gold Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Zweitveröffentlichung in der Schriftenreihe Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 1300
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.