Open source machine-learning algorithms for the prediction of optimal cancer drug therapies
Publication Date
October 26, 2017
Authors
Cai Huang, Roman Mezencev, John F. Mc Donald & Fredrik Vannberg
Volume
12
Issue
10
Pages
e0186906
DOI
https://dx.plos.org/10.1371/journal.pone.0186906
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0186906
Scopus
85032486392
Mendeley
http://www.mendeley.com/research/open-source-machinelearning-algorithms-prediction-optimal-cancer-drug-therapies
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Mendeley | Further Information

{"title"=>"Open source machine-learning algorithms for the prediction of optimal cancer drug therapies", "type"=>"journal", "authors"=>[{"first_name"=>"Cai", "last_name"=>"Huang", "scopus_author_id"=>"57196275477"}, {"first_name"=>"Roman", "last_name"=>"Mezencev", "scopus_author_id"=>"6603631387"}, {"first_name"=>"John F.", "last_name"=>"McDonald", "scopus_author_id"=>"35319980800"}, {"first_name"=>"Fredrik", "last_name"=>"Vannberg", "scopus_author_id"=>"13403956500"}], "year"=>2017, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "doi"=>"10.1371/journal.pone.0186906", "sgr"=>"85032486392", "scopus"=>"2-s2.0-85032486392", "isbn"=>"19326203 (Electronic)", "pmid"=>"29073279", "pui"=>"618945188"}, "id"=>"a0e8d30c-49a4-3970-b762-657dfbf1e96a", "abstract"=>"Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be \"drivers\" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm \"open source\", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.", "link"=>"http://www.mendeley.com/research/open-source-machinelearning-algorithms-prediction-optimal-cancer-drug-therapies", "reader_count"=>38, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Student > Doctoral Student"=>1, "Researcher"=>19, "Student > Ph. D. Student"=>7, "Other"=>2, "Student > Master"=>4, "Student > Bachelor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Student > Doctoral Student"=>1, "Researcher"=>19, "Student > Ph. D. Student"=>7, "Other"=>2, "Student > Master"=>4, "Student > Bachelor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>4, "Medicine and Dentistry"=>12, "Agricultural and Biological Sciences"=>7, "Arts and Humanities"=>1, "Chemistry"=>1, "Computer Science"=>4, "Immunology and Microbiology"=>1, "Economics, Econometrics and Finance"=>1, "Nursing and Health Professions"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>12}, "Chemistry"=>{"Chemistry"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>4}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "group_count"=>1}

Scopus | Further Information

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