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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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- Tan, BX, et al.
(författare)
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Assessing the Efficacy of Mdm2/Mdm4-Inhibiting Stapled Peptides Using Cellular Thermal Shift Assays
- 2015
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 5, s. 12116-
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Tidskriftsartikel (refereegranskat)abstract
- Previous publications on stapled peptide inhibitors against Mdm2/Mdm4-p53 interactions have established that this new class of drugs have the potential to be easily optimised to attain high binding affinity and specificity, but the mechanisms controlling their cellular uptake and target engagement remain elusive and controversial. To aid in understanding the rules of peptide and staple design and to enable rapid optimisation, we employed the newly-developed cellular thermal shift assay (CETSA). CETSA was able to validate stapled peptide binding to Mdm2 and Mdm4 and the method was also used to determine the extent of cellular uptake, cellular availability and intracellular binding of the endogenous target proteins in its native environment. Our data suggest that while the stapled peptides engage their targets intracellularly, more work is needed to improve their cellular entry and target engagement efficiency in vivo. CETSA now provides a valuable tool to optimize such in vivo properties of stapled peptides.
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