ArticleCell cycle (Georgetown, Tex.)2018
A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.
Article in Cell cycle (Georgetown, Tex.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 77 citations in OpenAlex.
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- Identification of Feature Genes of a Novel Neural Network Model for Bladder Cancer.Frontiers in genetics · 2022Article
- Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.Computational intelligence and neuroscience · 2022Article
- Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker.Oncology letters · 2021Article
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- Role of NRP1 in Bladder Cancer Pathogenesis and Progression.Frontiers in oncology · 2021Article
- Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways.Frontiers in genetics · 2021Article
- Pharmacogenomics of Impaired Tyrosine Kinase Inhibitor Response: Lessons Learned From Chronic Myelogenous Leukemia.Frontiers in pharmacology · 2021Review
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs - kinase inhibitors, such as imatinib or sorafenib.
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