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

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i911-i918
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

Abstract Motivation Accurate 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. Results In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network 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. Availability and implementation DeepCDR is freely available at https://github.com/kimmo1019/DeepCDR. Supplementary information Supplementary data are available at Bioinformatics online.

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.

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2019 ◽  
Vol 35 (19) ◽  
pp. 3846-3848 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Training Algorithms ◽  
Anti Cancer ◽  
Model Training ◽  
Response Modeling

Abstract Summary Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and implementation FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE and https://doi.org/10.17605/OSF.IO/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3. Supplementary information Supplementary data are available at Bioinformatics online.

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 FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2018 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary File ◽  
Data Sets ◽  
Training Algorithms ◽  
Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

Anti-cancer Drug Design

2018 ◽  
Vol 09 ◽  
Author(s):  
M SISSAOUI
Keyword(s):  
Drug Design ◽  
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.

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