ArticleScientific reports2016
Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- Machine learning-based prediction of drug response in ischemia reperfusion animal model.Scientific reports · 2025Article
- BANDRP: a bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics.Briefings in bioinformatics · 2024Article
- Article
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
- Deep learning and multi-omics approach to predict drug responses in cancer.BMC bioinformatics · 2022Article
- Development of machine learning classifiers to predict compound activity on prostate cancer cell lines.Journal of cheminformatics · 2022Article
- The molecular characteristics of gastric cancer patients living in Qinghai-Tibetan Plateau.BMC gastroenterology · 2022Article
- Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways.Frontiers in bioinformatics · 2021Article
- Computational Prediction of Drug Responses in Cancer Cell Lines From Cancer Omics and Detection of Drug Effectiveness Related Methylation Sites.Frontiers in genetics · 2020Article
- The Clinical and Molecular Characterization of Gastric Cancer Patients in Qinghai-Tibetan Plateau.Frontiers in oncology · 2020Article
- Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.Molecular therapy. Nucleic acids · 2019Article
- Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.BMC bioinformatics · 2019Article
- A novel heterogeneous network-based method for drug response prediction in cancer cell lines.Scientific reports · 2018Article
- Nonlinear mixed effects dose response modeling in high throughput drug screens: application to melanoma cell line analysis.Oncotarget · 2018Article
- Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data.F1000Research · 2016Article
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Authors and funding
3 authors.
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Abstract
Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell 'A549_LUNG' and compound 'Topotecan'. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.
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