A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency
Autor: | Olga Kovalchuk, Anton Buzdin, Alex Zhavoronkov, Victor Tkachev, Maria Suntsova, Ilya Muchnik, Nicolas Borisov |
---|---|
Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
Sorafenib Databases Factual Antineoplastic Agents Biology Machine learning computer.software_genre Machine Learning 03 medical and health sciences 0302 clinical medicine medicine Humans Precision Medicine Molecular Biology business.industry Cell growth Myeloid leukemia Cancer Imatinib Cell Biology medicine.disease Kidney Neoplasms Gene Expression Regulation Neoplastic Gene expression profiling 030104 developmental biology ROC Curve Area Under Curve 030220 oncology & carcinogenesis Adenocarcinoma Personalized medicine Artificial intelligence business computer Reports Developmental Biology medicine.drug |
Zdroj: | Cell Cycle. 17:486-491 |
ISSN: | 1551-4005 1538-4101 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |