| 1. |
- Bravo, L, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 2. |
- Tabiri, S, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 8. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 9. |
- Niemi, MEK, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 10. |
- Aad, G., et al.
(författare)
-
Studies of the performance of the ATLAS detector using cosmic-ray muons
- 2011
-
Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 71:3
-
Tidskriftsartikel (refereegranskat)abstract
- Muons from cosmic-ray interactions in the atmosphere provide a high-statistics source of particles that can be used to study the performance and calibration of the ATLAS detector. Cosmic-ray muons can penetrate to the cavern and deposit energy in all detector subsystems. Such events have played an important role in the commissioning of the detector since the start of the installation phase in 2005 and were particularly important for understanding the detector performance in the time prior to the arrival of the first LHC beams. Global cosmic-ray runs were undertaken in both 2008 and 2009 and these data have been used through to the early phases of collision data-taking as a tool for calibration, alignment and detector monitoring. These large datasets have also been used for detector performance studies, including investigations that rely on the combined performance of different subsystems. This paper presents the results of performance studies related to combined tracking, lepton identification and the reconstruction of jets and missing transverse energy. Results are compared to expectations based on a cosmic-ray event generator and a full simulation of the detector response.
|
|
| 11. |
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
|
|
| 15. |
|
|
| 16. |
|
|
| 17. |
- Kilpelainen, TO, et al.
(författare)
-
Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity
- 2019
-
Ingår i: Nature communications. - London : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 376-
-
Tidskriftsartikel (refereegranskat)abstract
- Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.
|
|
| 18. |
|
|
| 19. |
|
|
| 20. |
|
|
| 21. |
- Romagnoni, A, et al.
(författare)
-
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
-
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.
|
|
| 22. |
|
|
| 23. |
|
|
| 24. |
|
|
| 25. |
|
|
| 26. |
|
|
| 27. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 28. |
- Aad, G., et al.
(författare)
-
- 2011
-
Tidskriftsartikel (refereegranskat)
|
|
| 29. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 30. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 31. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 32. |
- Aad, G., et al.
(författare)
-
- 2013
-
swepub:Mat__t (refereegranskat)
|
|
| 33. |
- Aad, G., et al.
(författare)
-
- 2013
-
Ingår i: New Journal of Physics. - : IOP Publishing. - 1367-2630. ; 15
-
Tidskriftsartikel (refereegranskat)
|
|
| 34. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 35. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 36. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 37. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 38. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 39. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 40. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 41. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 42. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 43. |
- Aad, G., et al.
(författare)
-
- 2012
-
Tidskriftsartikel (refereegranskat)
|
|
| 44. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 45. |
- Aad, G., et al.
(författare)
-
- 2013
-
swepub:Mat__t (refereegranskat)
|
|
| 46. |
- Aad, G., et al.
(författare)
-
- 2012
-
Tidskriftsartikel (refereegranskat)
|
|
| 47. |
- Aad, G., et al.
(författare)
-
- 2012
-
Tidskriftsartikel (refereegranskat)
|
|
| 48. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 49. |
- Aad, G., et al.
(författare)
-
- 2012
-
swepub:Mat__t (refereegranskat)
|
|
| 50. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
