Journal of Arthur Bowes Smyth, 1787 March 22-1789 August [manuscript].

Manuscript reference no.: MS 4568.; The date of the final entry in this journal is uncertain. The last headed entry in the journal is dated 8th August 1789 although the final page of the journal appears to describe events taking place on the 11th.; Photocopy available for reference.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.ms-ms4568; The journal of Arthur Bowes Smyth, surgeon, Lady Penrhyn, 1787-1789 / edited by Paul G. Fidlon and R.J. Ryan. Sydney : Australian Documents Library, 1979.; Exhibited: "Treasures Gallery", National Library of Australia, 7 October 2011 - 15 December 2012. AuCNL. MS 4568 comprises the journal relating to Arthur Bowes Smyth's voyage to Australia in 1787, his stay in New South Wales and the trip back to England in 1789. This original diary provides a detailed and often entertaining account of the voyage to Australia, the early weeks of the settlement at Port Jackson and a visit to Lord Howe Island. The entry for 26 January 1788 describes the encounter in Botany Bay with the French expedition commanded by La Perouse and Smyth's first impression of Port Jackson (1 v., 2 folders)

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