0000000001241633
AUTHOR
Z. Su
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
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 ne…
Development and analysis of the Soil Water Infiltration Global database
27 Pags.- 11 Tabls.- 8 Figs. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.
Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356
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