ArticleNature communications2019
Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records.medRxiv : the preprint server for health sciences · 2025Article
- A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method.Molecular diversity · 2024Article
- Microbiome-based classification models for fresh produce safety and quality evaluation.Microbiology spectrum · 2024Article
- Precision oncology: a review to assess interpretability in several explainable methods.Briefings in bioinformatics · 2023Review
- Article
- Nanoparticle delivery of miR-21-3p sensitizes melanoma to anti-PD-1 immunotherapy by promoting ferroptosis.Journal for immunotherapy of cancer · 2022Article
- Machine learning approach informs biology of cancer drug response.BMC bioinformatics · 2022Article
- Dynamic Meta-data Network Sparse PCA for Cancer Subtype Biomarker Screening.Frontiers in genetics · 2022Article
- Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.Frontiers in bioinformatics · 2022Review
- Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.Nature cancer · 2021Article
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine.Cancers · 2020Review
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.
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