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Search: WFRF:(Gao PY)

  • Result 1-9 of 9
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  • Alcorn, J, et al. (author)
  • Basic instrumentation for Hall A at Jefferson Lab
  • 2004
  • In: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - : Elsevier BV. - 0167-5087 .- 0168-9002. ; 522:3, s. 294-346
  • Journal article (peer-reviewed)abstract
    • The instrumentation in Hall A at the Thomas Jefferson National Accelerator Facility was designed to study electro-and photo-induced reactions at very high luminosity and good momentum and angular resolution for at least one of the reaction products. The central components of Hall A are two identical high resolution spectrometers, which allow the vertical drift chambers in the focal plane to provide a momentum resolution of better than 2 x 10(-4). A variety of Cherenkov counters, scintillators and lead-glass calorimeters provide excellent particle identification. The facility has been operated successfully at a luminosity well in excess of 10(38) CM-2 s(-1). The research program is aimed at a variety of subjects, including nucleon structure functions, nucleon form factors and properties of the nuclear medium. (C) 2003 Elsevier B.V. All rights reserved.
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  • Menden, MP, et al. (author)
  • Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
  • 2019
  • In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
  • Journal article (peer-reviewed)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.
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  • Aad, G., et al. (author)
  • 2011
  • swepub:Mat__t (peer-reviewed)
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  • Result 1-9 of 9

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