scholarly journals Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

Oncotarget ◽  
2018 ◽  
Vol 9 (32) ◽  
pp. 22546-22558 ◽  
Author(s):  
Mengying Zhang ◽  
Christian Saad ◽  
Lien Le ◽  
Kathrin Halfter ◽  
Bernhard Bauer ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Computational Modeling ◽  
Drug Response ◽  
Response Prediction ◽  
Cancer Drug ◽  
Anti Cancer ◽  
Methionine Cycle

AttDRP: Attention Mechanism-based Model for Anti-Cancer Drug Response Prediction

2021 ◽  
Vol 48 (6) ◽  
pp. 713-722
Author(s):  
Jonghwan Choi ◽  
Sangmin Seo ◽  
Sanghyun Park
Keyword(s):  
Drug Response ◽  
Response Prediction ◽  
Attention Mechanism ◽  
Cancer Drug ◽  
Anti Cancer

scholarly journals Ensemble transfer learning for the prediction of anti-cancer drug response

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yitan Zhu ◽  
Thomas Brettin ◽  
Yvonne A. Evrard ◽  
Alexander Partin ◽  
Fangfang Xia ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Transfer Learning ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Cancer Drug ◽  
Precision Oncology ◽  
Learning Framework ◽  
Prediction Algorithms ◽  
Anti Cancer

Abstract Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.

Identifying DNA Methylation Biomarkers of Cancer Drug Response

2005 ◽  
Vol 5 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Sabine Maier ◽  
Christina Dahlstroem ◽  
Carolina Haefliger ◽  
Achim Plum ◽  
Christian Piepenbrock
Keyword(s):  
Dna Methylation ◽  
Drug Response ◽  
Cancer Drug
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