| 1. |
- Aad, G, et al.
(författare)
-
- 2015
-
swepub:Mat__t
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Ferreira, MA, et al.
(författare)
-
Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer
- 2019
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 1741-
-
Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.
|
|
| 5. |
|
|
| 6. |
- Ahdida, C., et al.
(författare)
-
Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
- 2019
-
Ingår i: Journal of Instrumentation. - : IOP PUBLISHING LTD. - 1748-0221 .- 1748-0221. ; 14
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam dump and which travel distances between fifty metres and tens of kilometers. The SHiP detector needs to operate under ultra-low background conditions and requires large simulated samples of muon induced background processes. Through the use of Generative Adversarial Networks it is possible to emulate the simulation of the interaction of 400 GeV/c proton beams with the SHiP target, an otherwise computationally intensive process. For the simulation requirements of the SHiP experiment, generative networks are capable of approximating the full simulation of the dense fixed target, offering a speed increase by a factor of O(10(6)). To evaluate the performance of such an approach, comparisons of the distributions of reconstructed muon momenta in SHiP's spectrometer between samples using the full simulation and samples produced through generative models are presented. The methods discussed in this paper can be generalised and applied to modelling any non-discrete multi-dimensional distribution.
|
|
| 7. |
- Ahdida, C., et al.
(författare)
-
Sensitivity of the SHiP experiment to Heavy Neutral Leptons
- 2019
-
Ingår i: Journal of High Energy Physics (JHEP). - 1126-6708 .- 1029-8479. ; :4
-
Tidskriftsartikel (refereegranskat)abstract
- Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate. The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O(50 m) and tens of kilometers before decaying. We present the sensitivity of the SHiP experiment to a number of HNL's benchmark models and provide a way to calculate the SHiP's sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.
|
|
| 8. |
- Ahdida, C., et al.
(författare)
-
The experimental facility for the Search for Hidden Particles at the CERN SPS
- 2019
-
Ingår i: Journal of Instrumentation. - : Institute of Physics Publishing (IOPP). - 1748-0221 .- 1748-0221. ; 14
-
Tidskriftsartikel (refereegranskat)abstract
- The Search for Hidden Particles (SHiP) Collaboration has shown that the CERN SPS accelerator with its 400 GeV/c proton beam offers a unique opportunity to explore the Hidden Sector [1-3]. The proposed experiment is an intensity frontier experiment which is capable of searching for hidden particles through both visible decays and through scattering signatures from recoil of electrons or nuclei. The high-intensity experimental facility developed by the SHiP Collaboration is based on a number of key features and developments which provide the possibility of probing a large part of the parameter space for a wide range of models with light long-lived super-weakly interacting particles with masses up to O(10) GeV/c(2) in an environment of extremely clean background conditions. This paper describes the proposal for the experimental facility together with the most important feasibility studies. The paper focuses on the challenging new ideas behind the beam extraction and beam delivery, the proton beam dump, and the suppression of beam-induced background.
|
|
| 9. |
|
|
| 10. |
- Menden, MP, et al.
(författare)
-
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- 2019
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
-
Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
|
|
