3 research outputs found
Sharon Pollock's Portraits of the Artist
This essay is a study of Sharon Pollock's artist characters. Using concepts drawn from contemporary autobiography theory with Butler's theory of performativity, I examine three plays in detail—Blood Relations, Saucy Jack, and Moving Pictures—to show how Pollock develops an artist's power to produce identity, to articulate the meaning of a life in performance, and, in some instances, to resist those scriptings of life which limit or erase individuals. While I conclude with observations about these three plays, I also suggest that the paradigm Pollock explores through the artist character informs other of her plays and non-artist characters.
Dans cet étude, on examine les portraits des artistes présentees par Sharon Pollock dans ses pièces, mais avec attention particulièr a Moving Pictures, qui raconte l'histoire de Nell Shipman, actrice et realisatrice. Pour creer la character de "Shipman," Pollock a utiliser l'autobiographie de Shipman et, dans mon analyse, j'applique les théories contemporaines d'autobiographics et performativity pour explorer comment Pollock monte l'enquete autobiographique de son "Shipman.
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers