Exile Vol. XLIII No. 2

41st Year Title Page i Epigraphy by Ezra Pound ii Table of Contents iii / Contributors\u27 Notes 70-71 Editorial Board 72-73 ART Untitled by Kari Hernquist \u2799 4 Talking Out my Window by Heather Trabert \u2797 13 Renamed I by Ben Blake \u2797 18 photo paint by alex e. blazer \u2797 23 Butterfly by Mary Donnelley \u2797 32 unabridged by alex e. blazer \u2797 37 Holding Me In by Heather Trabert \u2797 43 Untitled by Kari Hernquist \u2797 55 Untitled by Camille Gammon-Hittelman \u2799 61 Stars by Mary Donnelley \u2797 69 POETRY Victrola by erin c. malone \u2799 1 All by Kellam Ayres \u2797 2-3 curtailed sun in the net by alex e. blazer \u2797 5 the weaker sex by Bekah Taylor \u2700 6 A poem concerning a silent manifesto by Colin Bossen \u2798 14 Father by Alison Stine \u2700 15 Vacant by Sean Boyle \u2700 16 Ecstasy by Amy spears \u2798 17 Seven Haikus by Jen Suster \u2797 21 Pages from a Diary by Trish Klei \u2797 22 Watching an Ageless Woman and an Ancient Trade by Heather Trabert \u2797 24-25 Still Waters by Jay Brandeis \u2799 26 just shy of freedom by Sean Boyle \u2700 36 [Touch the mothers you never knew] by Heather Trabert \u2797 38 Fishing for Meaning by Bekah Taylor \u2700 39 the novel by Sara Brown \u2799 40-41 annihilation by erin c. malone \u2799 42 Upon Enlistment by Trish Klei \u2797 44 the expatriate by erin c. malone \u2799 47 Rockettes by Trish Klei \u2797 48-49 Abstraction by Colin Bossen \u2798 54 always kinesis by alex e. blazer \u2797 56-57 Lily by Alison Stine \u2700 58-59 Falling In by Bekah Taylor \u2700 60 this bird has flown by paul durica \u2700 62-63 exfoliating some sun by alex e. blazer \u2797 64 Liberation: May 8, 1945 by Jen suster \u2797 65 PROSE Journal: 12 December 1996 through 15 January 1997 by Lynn Tramonte \u2798 7-12 Ash by paul durica \u2700 19-20 Birdhouse by Tyler Smith \u2797 27-35 Party in December by paul durica \u2700 45-46 Smoke Circles by Alison Stine \u2700 50-53 Seal by Lynn Tramonte \u2798 66-68 All submissions are reviewed on an anonymous basis, and all editorial decisions are shared equally among the members of the Editorial Board. -72 Cover art Toy Child by Ben Blake \u2797 -7

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