ArticlePLoS computational biology2015
Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 224 citations in OpenAlex.
- A Pharmacogenetic Prediction Model of Progression-Free Survival in Breast Cancer using Genome-Wide Genotyping Data from CALGB 40502 (Alliance).Clinical pharmacology and therapeutics · 2019Trial
- Machine learning-based prediction of drug response in ischemia reperfusion animal model.Scientific reports · 2025Article
- DVFGCDR: a dual-view fusion graph neural network for cancer drug response prediction.NAR cancer · 2025Article
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
- Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning.International journal of molecular sciences · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.PLoS computational biology · 2025Article
- Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.Briefings in bioinformatics · 2024Article
- MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.Journal of integrative bioinformatics · 2024Article
- Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.Statistics in biosciences · 2024Article
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- Reliable anti-cancer drug sensitivity prediction and prioritization.Scientific reports · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.BMC bioinformatics · 2024Article
- Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers.PloS one · 2024Article
- GPDRP: a multimodal framework for drug response prediction with graph transformer.BMC bioinformatics · 2023Article
- Molecular data representation based on gene embeddings for cancer drug response prediction.Scientific reports · 2023Article
- NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.Diagnostics (Basel, Switzerland) · 2023Article
- Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.