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Sökning: WFRF:(Kristensen J) > Kungliga Tekniska Högskolan

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1.
  • Aidas, Kestutis, et al. (författare)
  • The Dalton quantum chemistry program system
  • 2014
  • Ingår i: Wiley Interdisciplinary Reviews. Computational Molecular Science. - : Wiley. - 1759-0876. ; 4:3, s. 269-284
  • Tidskriftsartikel (refereegranskat)abstract
    • Dalton is a powerful general-purpose program system for the study of molecular electronic structure at the Hartree-Fock, Kohn-Sham, multiconfigurational self-consistent-field, MOller-Plesset, configuration-interaction, and coupled-cluster levels of theory. Apart from the total energy, a wide variety of molecular properties may be calculated using these electronic-structure models. Molecular gradients and Hessians are available for geometry optimizations, molecular dynamics, and vibrational studies, whereas magnetic resonance and optical activity can be studied in a gauge-origin-invariant manner. Frequency-dependent molecular properties can be calculated using linear, quadratic, and cubic response theory. A large number of singlet and triplet perturbation operators are available for the study of one-, two-, and three-photon processes. Environmental effects may be included using various dielectric-medium and quantum-mechanics/molecular-mechanics models. Large molecules may be studied using linear-scaling and massively parallel algorithms. Dalton is distributed at no cost from for a number of UNIX platforms.
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2.
  • Johansson, Henrik J., et al. (författare)
  • Breast cancer quantitative proteome and proteogenomic landscape
  • 2019
  • Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 10
  • Tidskriftsartikel (refereegranskat)abstract
    • In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.
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