ArticleMolecular therapy. Nucleic acids2019
Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
- Gene dependency-informed inference of response to targeted cancer therapies.Nature communications · 2026Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- Multiomics-Driven Drug-Cell Interaction Network for Chemotherapy Sensitivity Prediction in Metabolically Defined Triple-Negative Breast Cancer Subtypes.Journal of cellular and molecular medicine · 2025Article
- SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction.bioRxiv : the preprint server for biology · 2025Article
- CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.BMC bioinformatics · 2025Article
- BANDRP: a bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics.Briefings in bioinformatics · 2024Article
- An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.Journal of personalized medicine · 2024Article
- Reliable anti-cancer drug sensitivity prediction and prioritization.Scientific reports · 2024Article
- Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.Environmental science & technology · 2024Article
- DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.PLoS computational biology · 2024Article
- Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.Interdisciplinary sciences, computational life sciences · 2024Article
- Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma.PloS one · 2024Article
- Cancer drug response prediction with surrogate modeling-based graph neural architecture search.Bioinformatics (Oxford, England) · 2023Article
- 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
- DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer.International journal of molecular sciences · 2022Article
- Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.Scientific reports · 2022Article
- Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.Journal of cellular and molecular medicine · 2022Article
- In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts.Journal of mammary gland biology and neoplasia · 2022Review
- Integrated Drug Mining Reveals Actionable Strategies Inhibiting Plexiform Neurofibromas.Brain sciences · 2022Article
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6 authors.
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Abstract
Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the "omic" diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.
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