ArticleBMC bioinformatics2019
Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- DNA Repair Genes as Drug Candidates for Early Breast Cancer Onset in Latin America: A Systematic Review.International journal of molecular sciences · 2021Pooled it
- Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.Journal of the American Statistical Association · 2024Article
- Graph neural networks are promising for phenotypic virtual screening on cancer cell lines.Biology methods & protocols · 2024Article
- StringFix: an annotation-guided transcriptome assembler improves the recovery of amino acid sequences from RNA-Seq reads.Genes & genomics · 2023Article
- A performance evaluation of drug response prediction models for individual drugs.Scientific reports · 2023Article
- Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.BMC bioinformatics · 2023Article
- Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.Frontiers in genetics · 2023Article
- PredictiveNetwork: predictive gene network estimation with application to gastric cancer drug response-predictive network analysis.BMC bioinformatics · 2022Article
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.International journal of molecular sciences · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- A cross-study analysis of drug response prediction in cancer cell lines.Briefings in bioinformatics · 2022Article
- Dissecting the Genome for Drug Response Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks.PloS one · 2022Article
- Predicting breast cancer drug response using a multiple-layer cell line drug response network model.BMC cancer · 2021Article
- Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines.BMC genomics · 2021Article
- Exploration of databases and methods supporting drug repurposing: a comprehensive survey.Briefings in bioinformatics · 2021Review
- Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression.Genomics & informatics · 2021Article
- A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.PloS one · 2021Article
- Deep learning of pharmacogenomics resources: moving towards precision oncology.Briefings in bioinformatics · 2020Article
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4 authors.
Funding
Abstract
backgroundAccurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data.
resultsWe first demonstrated an observation on the CCLE and GDSC datasets, i.e., genetically similar cell lines always exhibit higher response correlations to structurally related drugs. Based on this observation we built a cell line-drug complex network model, named CDCN model. It captures different contributions of all available cell line-drug responses through cell line similarities and drug similarities. We executed anticancer drug response prediction on CCLE and GDSC independently. The result is significantly superior to that of some existing studies. More importantly, our model could predict the response of new drug to new cell line with considerable performance. We also divided all possible cell lines into "sensitive" and "resistant" groups by their response values to a given drug, the prediction accuracy, sensitivity, specificity and goodness of fit are also very promising.
conclusionCDCN model is a comprehensive tool to predict anticancer drug responses. Compared with existing methods, it is able to provide more satisfactory prediction results with less computational consumption.
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