scholarly journals A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction

2018 ◽  
Vol 9 ◽  
Author(s):  
Lin Zhang ◽  
Xing Chen ◽  
Na-Na Guan ◽  
Hui Liu ◽  
Jian-Qiang Li
Keyword(s):  
Collaborative Filtering ◽  
Drug Response ◽  
Response Prediction ◽  
Cancer Drug ◽  
Filtering Method ◽  
Anti Cancer

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.

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

scholarly journals DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

2020 ◽  
Author(s):  
Qiao Liu ◽  
Zhiqiang Hu ◽  
Rui Jiang ◽  
Mu Zhou
Keyword(s):  
Drug Design ◽  
Cancer Biology ◽  
Drug Response ◽  
Drug Efficacy ◽  
Supplementary Information ◽  
Cancer Drug ◽  
Convolutional Network ◽  
Chemical Structures ◽  
Anti Cancer ◽  
Cancer Types

AbstractMotivationAccurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology.ResultsIn this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting cancer drug response. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network (UGCN) and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design.AvailabilityDeepCDR is freely available at https://github.com/kimmo1019/[email protected]; [email protected] informationSupplementary data are available at Bioinformatics online.

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