ArticleBMC bioinformatics2018
Application of transfer learning for cancer drug sensitivity prediction.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- Gene Signatures and Oncology Treatment Implications.Hematology/oncology clinics of North America · 2025Review
- A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning.International journal of molecular sciences · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Transfer learning for genotype-phenotype prediction using deep learning models.BMC bioinformatics · 2022Article
- Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data.Nature communications · 2022Article
- Identification of phenocopies improves prediction of targeted therapy response over DNA mutations alone.NPJ genomic medicine · 2022Article
- Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.Scientific reports · 2022Article
- A cross-study analysis of drug response prediction in cancer cell lines.Briefings in bioinformatics · 2022Article
- Article
- Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.NPJ genomic medicine · 2021Article
- Learning curves for drug response prediction in cancer cell lines.BMC bioinformatics · 2021Article
- Anticancer drug synergy prediction in understudied tissues using transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Article
- Ensemble transfer learning for the prediction of anti-cancer drug response.Scientific reports · 2020Article
- From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health.Journal of personalized medicine · 2020Review
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundIn precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context.
resultsIn this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the two databases. Utilizing CCLE and GDSC databases, we illustrate that the proposed approaches accomplish a better prediction of drug sensitivities for different scenarios as compared to the existing approaches.
conclusionWe have compared the performance of the proposed predictive models with database-specific individual models as well as existing transfer learning approaches. We note that our proposed approaches exhibit superior performance compared to the abovementioned alternative techniques for predicting sensitivity for different anti-cancer compounds, particularly the nonlinear mapping model shows the best overall performance.
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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.