0000000000110056
AUTHOR
H. Blackburn
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…
CCDC 116646: Experimental Crystal Structure Determination
Related Article: H.Blackburn, J.C.Fettinger, H.-B.Kraatz, R.Poli, R.C.Torralba|2000|J.Organomet.Chem.|593|27|doi:10.1016/S0022-328X(99)00292-2
CCDC 116645: Experimental Crystal Structure Determination
Related Article: H.Blackburn, J.C.Fettinger, H.-B.Kraatz, R.Poli, R.C.Torralba|2000|J.Organomet.Chem.|593|27|doi:10.1016/S0022-328X(99)00292-2
CCDC 116644: Experimental Crystal Structure Determination
Related Article: H.Blackburn, J.C.Fettinger, H.-B.Kraatz, R.Poli, R.C.Torralba|2000|J.Organomet.Chem.|593|27|doi:10.1016/S0022-328X(99)00292-2