A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data
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{"title"=>"A feature selection algorithm to compute gene centric methylation from probe level methylation data", "type"=>"journal", "authors"=>[{"first_name"=>"Brittany", "last_name"=>"Baur", "scopus_author_id"=>"55277955700"}, {"first_name"=>"Serdar", "last_name"=>"Bozdag", "scopus_author_id"=>"24472576200"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84960510969", "pmid"=>"26872146", "doi"=>"10.1371/journal.pone.0148977", "pui"=>"608920797", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84960510969"}, "id"=>"f91ee92b-137d-3f3a-8fe4-b1f02c42a4c5", "abstract"=>"DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.", "link"=>"http://www.mendeley.com/research/feature-selection-algorithm-compute-gene-centric-methylation-probe-level-methylation-data", "reader_count"=>8, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>3, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>1, "Psychology"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>0}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/3931348"], "description"=>"<p>A) MCC, B) Precision, C) Recall, D) Specificity. Green squares specify the median and the red pluses specify the mean. NB: Naive Bayes, DT: Decision tree, SVM: Support Vector Machine</p>", "links"=>[], "tags"=>["feature selection algorithms", "DNA methylation", "effects gene expression", "transcriptional activities", "DNA methylation changes", "Gene Centric Methylation", "Gene Ontology terms", "Feature Selection Algorithm", "support vector machines", "DNA methylation data", "Illumina Infinium HumanMethylation 450 K array", "mRNA expression levels", "feature selection algorithm", "probe level DNA methylation data", "Probe Level Methylation Data DNA methylation"], "article_id"=>2294227, "categories"=>["Genetics", "Molecular Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Mathematical Sciences not elsewhere classified", "Cancer", "Infectious Diseases", "Plant Biology"], "users"=>["Brittany Baur", "Serdar Bozdag"], "doi"=>"https://dx.doi.org/2294227", "stats"=>{"downloads"=>1, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Violin_plots_of_performance_metrics_for_the_algorithm_when_utilizing_different_classification_methods_in_the_SFS_algorithm_and_controls_on_the_breast_cancer_cell_line_data_/2294227", "title"=>"Violin plots of performance metrics for the algorithm when utilizing different classification methods in the SFS algorithm and controls on the breast cancer cell line data.", "pos_in_sequence"=>2, "defined_type"=>1, "published_date"=>"2016-02-12 13:32:03"}
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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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