ArticleBMC cancer2017
Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.
Article in BMC cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed, 185 citations in OpenAlex.
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- Critical evaluation of drug response prediction models with DrEval.Nature communications · 2026Article
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- MoGraphDRP: Multi-omics and graph fusion with bilinear attention for predicting drug sensitivity.PloS one · 2026Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 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
- Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Distinct dysregulated pathways in sporadic and Lynch syndrome-associated colorectal cancer offer insights for targeted treatment.FEBS letters · 2025Article
- MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.BMC bioinformatics · 2025Article
- Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.Scientific reports · 2025Article
- ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.PLoS computational biology · 2025Article
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery.NPJ precision oncology · 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
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.PLoS computational biology · 2024Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHuman cancer cell lines are used in research to study the biology of cancer and to test cancer treatments. Recently there are already some large panels of several hundred human cancer cell lines which are characterized with genomic and pharmacological data. The ability to predict drug responses using these pharmacogenomics data can facilitate the development of precision cancer medicines. Although several methods have been developed to address the drug response prediction, there are many challenges in obtaining accurate prediction.
methodsBased on the fact that similar cell lines and similar drugs exhibit similar drug responses, we adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using chemical structures of drugs and baseline gene expression levels in cell lines. Specifically, chemical structural similarity of drugs and gene expression profile similarity of cell lines were considered as regularization terms, which were incorporated to the drug response matrix factorization model.
resultsWe first demonstrated the effectiveness of SRMF using a set of simulation data and compared it with two typical similarity-based methods. Furthermore, we applied it to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets, and performance of SRMF exceeds three state-of-the-art methods. We also applied SRMF to estimate the missing drug response values in the GDSC dataset. Even though SRMF does not specifically model mutation information, it could correctly predict drug-cancer gene associations that are consistent with existing data, and identify novel drug-cancer gene associations that are not found in existing data as well. SRMF can also aid in drug repositioning. The newly predicted drug responses of GDSC dataset suggest that mTOR inhibitor rapamycin was sensitive to non-small cell lung cancer (NSCLC), and expression of AK1RC3 and HINT1 may be adjunct markers of cell line sensitivity to rapamycin.
conclusionsOur analysis showed that the proposed data integration method is able to improve the accuracy of prediction of anticancer drug responses in cell lines, and can identify consistent and novel drug-cancer gene associations compared to existing data as well as aid in drug repositioning.
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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.