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- Ahrens J, Andres E, Bai X, Barouch G, Barwick SW, Bay RC, Becka T, Becker KH, Bertrand D, Binon F, Biron A, Booth J, Botner O, Bouchta A, Bouhali O, Boyce MM, Carius S, Chen A, Chirkin D, Conrad J, Cooley J, Costa CGS, Cowen DF, Dalberg E, De Clercq C, De
(författare)
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OBSERVATION OF HIGH-ENERGY ATMOSPHERIC NEUTRINOS WITH THE ANTARCTIC MUON AND NEUTRINO DETECTOR ARRAY.
- 2002
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Ingår i: Phys. Rev.. - 0556-2821. ; D66, s. 012005-
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Tidskriftsartikel (refereegranskat)abstract
- The Antarctic Muon and Neutrino Detector Array (AMANDA) began collecting data with ten strings in 1997. Results from the first year of operation are presented. Neutrinos coming through the Earth from the Northern Hemisphere are identified by secondary muo
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- Andres, E, et al.
(författare)
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Observation of high-energy neutrinos using Cerenkov detectors embedded deep in Antarctic ice
- 2001
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Ingår i: NATURE. - : MACMILLAN PUBLISHERS LTD. - 0028-0836. ; 410:6827, s. 441-443
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Tidskriftsartikel (refereegranskat)abstract
- Neutrinos are elementary particles that carry no electric charge and have little mass. As they interact only weakly with other particles, they can penetrate enormous amounts of matter, and therefore have the potential to directly convey astrophysical info
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- Andres, E, et al.
(författare)
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Results from the AMANDA high energy neutrino detector
- 2001
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Ingår i: NUCLEAR PHYSICS B-PROCEEDINGS SUPPLEMENTS. - : ELSEVIER SCIENCE BV. - 0920-5632. ; 91, s. 423-430
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Tidskriftsartikel (refereegranskat)abstract
- This paper briefly summarizes the search for astronomical sources of high-energy neutrinos using the AMANDA-B10 detector. The complete data set from 1997 was analyzed. Far E-mu > 10 TeV, the detector exceeds 10,000 m(2) in effective area between declinati
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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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