Symbolic Regression Modeling of Drug Responses

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@InProceedings{Fitzsimmons:2018:AI4I,

• author = "Jake Fitzsimmons and Pablo Moscato",
• title = "Symbolic Regression Modeling of Drug Responses",
• booktitle = "2018 First International Conference on Artificial Intelligence for Industries (AI4I)",
• year = "2018",
• pages = "52--59",
• address = "Laguna Hills, CA, USA",
• month = "26-28 " # sep,
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-5386-9463-3",
• DOI = "doi:10.1109/AI4I.2018.8665684",
• abstract = "Big pharmaceutical companies require to innovate by applying new machine learning and artificial intelligence methods to understand the large datasets produced by high-throughput technologies. In addition to reduce development costs for these industries, regression and classification models of drug response are needed for the final quest of delivering personalized treatment for cancer. An emphasis exists in developing models that allow for both prediction and ease of interpretation. In this contribution we present results obtained by symbolic regression. We employ a public domain dataset of drug responses on a large cancer cell line panel and compare with a previous method based on binarisation of the response data and the use of integer linear programming to find logic models. We present derived models of drug response for the drugs Afatinib, Dactolisib (BEZ235), Cytarabine, and Paclitaxel as well as for AZD6244, JQ12, KIN001-102, and PLX4720. We provide indication of the interpretability with a biological analysis of the results for Afatnib and Dactolisib, showing that our models introduce variables that point at known mechanisms of action of these drugs.",
• notes = "Also known as \cite{8665684}",

}

Genetic Programming entries for Jake Fitzsimmons Pablo Moscato

Citations