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- Schael, S., et al.
(författare)
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Electroweak measurements in electron positron collisions at W-boson-pair energies at LEP
- 2013
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Ingår i: Physics Reports. - : Elsevier BV. - 0370-1573 .- 1873-6270. ; 532:4, s. 119-244
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Forskningsöversikt (refereegranskat)abstract
- Electroweak measurements performed with data taken at the electron positron collider LEP at CERN from 1995 to 2000 are reported. The combined data set considered in this report corresponds to a total luminosity of about 3 fb(-1) collected by the four LEP experiments ALEPH, DELPHI, 13 and OPAL, at centre-of-mass energies ranging from 130 GeV to 209 GeV. Combining the published results of the four LEP experiments, the measurements include total and differential cross-sections in photon-pair, fermion-pair and four-fermion production, the latter resulting from both double-resonant WW and ZZ production as well as singly resonant production. Total and differential cross-sections are measured precisely, providing a stringent test of the Standard Model at centre-of-mass energies never explored before in electron positron collisions. Final-state interaction effects in four-fermion production, such as those arising from colour reconnection and Bose Einstein correlations between the two W decay systems arising in WW production, are searched for and upper limits on the strength of possible effects are obtained. The data are used to determine fundamental properties of the W boson and the electroweak theory. Among others, the mass and width of the W boson, m(w) and Gamma(w), the branching fraction of W decays to hadrons, B(W -> had), and the trilinear gauge-boson self-couplings g(1)(Z), K-gamma and lambda(gamma), are determined to be: m(w) = 80.376 +/- 0.033 GeV Gamma(w) = 2.195 +/- 0.083 GeV B(W -> had) = 67.41 +/- 0.27% g(1)(Z) = 0.984(-0.020)(+0.018) K-gamma - 0.982 +/- 0.042 lambda(gamma) = 0.022 +/- 0.019. (C) 2013 Elsevier B.V. All rights reserved.
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- Napolitano, F, et al.
(författare)
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Drug repositioning: a machine-learning approach through data integration
- 2013
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Ingår i: Journal of cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 5:1, s. 30-
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Tidskriftsartikel (refereegranskat)abstract
- Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.
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