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- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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- Padoa, CJ, et al.
(författare)
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Recombinant Fabs of human monoclonal antibodies specific to the middle epitope of GAD65 inhibit type 1 diabetes-specific GAD65Abs
- 2003
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 52:11, s. 2689-2695
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Tidskriftsartikel (refereegranskat)abstract
- Autoantibodies to the 65-kDa isoform of GAD (GAD65Abs) are associated with type 1 diabetes development, but the conformational nature of the GAD65Ab epitopes complicates the evaluation of disease risk. Six GAD65-specific recombinant Fabs (rFabs) were cloned from monoclonal antibodies b96.11, DP-C, DP-A, DPD, 144, and 221–442. The binding of GAD65Abs in 61 type 1 diabetic patients to GAD65 was analyzed by competitive radioimmunoassays with the six rFabs to ascertain disease-specific GAD65Ab binding specificities. The median binding was reduced significantly by rFab b96.11 (72%) (P < 0.0001), DP-A (84%) (P < 0.0001), DP-C (84%) (P < 0.0001), 221–442 (79%) (P < 0.0001), and DP-D (80%) (P < 0.0001). The competition pattern in type 1 diabetic patients differed from that in GAD65Ab-positive late autoimmune diabetes in adults (LADA) patients (n = 44), first-degree relatives (n = 38), and healthy individuals (n = 14). Whereas 87 and 72% of the type 1 diabetic sera were competed by rFab b96.11 and DP-C, respectively, only 34 and 26% of LADA patients, 18 and 25% of first-degree relatives, and 7 and 28% of healthy individuals showed competition (P < 0.0001). These findings support the view that type 1 diabetes is associated with disease- and epitope-specific GAD65Abs and supports the notion that the middle epitope is disease associated. These GAD65-specific rFabs should prove useful in predicting type 1 diabetes and in the study of conformational GAD65Ab epitopes.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)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.
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