scholarly journals Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

2021 ◽  
Vol 19 (1) ◽  
pp. e10
Author(s):  
Kexin Qiu ◽  
JoongHo Lee ◽  
HanByeol Kim ◽  
Seokhyun Yoon ◽  
Keunsoo Kang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cell Line ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Cancer Drug ◽  
Anti Cancer

scholarly journals Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

2022 ◽  
Vol 70 (2) ◽  
pp. 2743-2760
Author(s):  
Mehdi Hassan ◽  
Safdar Ali ◽  
Muhammad Sanaullah ◽  
Khuram Shahzad ◽  
Sadaf Mushtaq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Liver Cancer Cell ◽  
Liver Cancer Cell Line

scholarly journals Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways

2021 ◽  
Vol 1 ◽  
Author(s):  
Heming Zhang ◽  
Yixin Chen ◽  
Fuhai Li
Keyword(s):  
Deep Learning ◽  
Signaling Pathways ◽  
Cell Line ◽  
Cancer Patients ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Deep Learning Model ◽  
Anticancer Drug Response

Thanks to the availability of multiomics data of individual cancer patients, precision medicine or personalized medicine is becoming a promising treatment for individual cancer patients. However, the association patterns, that is, the mechanism of response (MoR) between large-scale multiomics features and drug response are complex and heterogeneous and remain unclear. Although there are existing computational models for predicting drug response using the high-dimensional multiomics features, it remains challenging to uncover the complex molecular mechanism of drug responses. To reduce the number of predictors/features and make the model more interpretable, in this study, 46 signaling pathways were used to build a deep learning model constrained by signaling pathways, consDeepSignaling, for anti–drug response prediction. Multiomics data, like gene expression and copy number variation, of individual genes can be integrated naturally in this model. The signaling pathway–constrained deep learning model was evaluated using the multiomics data of ∼1000 cancer cell lines in the Broad Institute Cancer Cell Line Encyclopedia (CCLE) database and the corresponding drug–cancer cell line response data set in the Genomics of Drug Sensitivity in Cancer (GDSC) database. The evaluation results showed that the proposed model outperformed the existing deep neural network models. Also, the model interpretation analysis indicated the distinctive patterns of importance of signaling pathways in anticancer drug response prediction.

scholarly journals kESVR: An Ensemble Model for Drug Response Prediction in Precision Medicine Using Cancer Cell Lines Gene Expression

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 844
Author(s):  
Abhishek Majumdar ◽  
Yueze Liu ◽  
Yaoqin Lu ◽  
Shaofeng Wu ◽  
Lijun Cheng
Keyword(s):  
Gene Expression ◽  
Support Vector Regression ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Cancer Cell Lines ◽  
Support Vector

Background: Cancer cell lines are frequently used in research as in-vitro tumor models. Genomic data and large-scale drug screening have accelerated the right drug selection for cancer patients. Accuracy in drug response prediction is crucial for success. Due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data to predict drug response in precision medicine. Method: A novelty k-means Ensemble Support Vector Regression (kESVR) is developed to predict each drug response values for single patient based on cell-line gene expression data. The kESVR is a blend of supervised and unsupervised learning methods and is entirely data driven. It utilizes embedded clustering (Principal Component Analysis and k-means clustering) and local regression (Support Vector Regression) to predict drug response and obtain the global pattern while overcoming missing data and outliers’ noise. Results: We compared the efficiency and accuracy of kESVR to 4 standard machine learning regression models: (1) simple linear regression, (2) support vector regression (3) random forest (quantile regression forest) and (4) back propagation neural network. Our results, which based on drug response across 610 cancer cells from Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP v2), proved to have the highest accuracy (smallest mean squared error (MSE) measure). We next compared kESVR with existing 17 drug response prediction models based a varied range of methods such as regression, Bayesian inference, matrix factorization and deep learning. After ranking the 18 models based on their accuracy of prediction, kESVR ranks first (best performing) in majority (74%) of the time. As for the remaining (26%) cases, kESVR still ranked in the top five performing models. Conclusion: In this paper we introduce a novel model (kESVR) for drug response prediction using high dimensional cell-line gene expression data. This model outperforms current existing prediction models in terms of prediction accuracy and speed and overcomes overfitting. This can be used in future to develop a robust drug response prediction system for cancer patients using the cancer cell-lines guidance and multi-omics data.

scholarly journals TCLP: an online cancer cell line catalogue integrating HLA type, predicted neo-epitopes, virus and gene expression

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Jelle Scholtalbers ◽  
Sebastian Boegel ◽  
Thomas Bukur ◽  
Marius Byl ◽  
Sebastian Goerges ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Hla Type

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
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