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Anti-Androgen Receptor(AR) antibody [ABT-AR]

Catalog No : V0007
  • Applications
    IHC, WB
  • Host Species
    Mouse
  • Reactivity
    Human
  • Size
    50ul
  • Price
    $175.00
Details
  • Product Name
    Anti-Androgen Receptor(AR) antibody [ABT-AR]
  • Catalog No
    V0007
  • Description
    Mouse monoclonal [ABT-AR] antibody to AR
  • Applications
    IHC, WB
  • Clonality
    Monoclonal
  • Host Species
    Mouse
  • Reactivity
    Human
  • ABT Clone ID
    ABT-AR
  • Antigen Retrieval
    Heat-induced epitope retrieval (HIER)
  • Cell Marque/Ventana Control
    Prostate
  • Dilution
    1:50-200
  • Entrez Gene ID (NCBI)
    367
  • IHC Recommended control
    Normal Prostate, Prostatic carcinoma
  • IHC Target Name
    Androgen Receptor(AR)
  • Immunogen
    Synthetic peptide
  • Localization
    Nuclear
  • OMIM
    313700
  • Purification
    Immunogen affinity purified
  • Research
    Prostate: prostate carcinoma, benign, Salivary Duct Carcinoma, breast carcinoma, prostate carcinoma, basal cell carcinoma, sebaceous carcinoma
  • Retrieval buffer
    TRIS-EDTA of pH8.0
  • Source/Ig Isotype
    Mouse IgG
  • Storage/Stablility
    -20°C/1 year
  • Synonyms
    ANDR, Androgen receptor, DHTR, Dihydrotestosterone receptor, NR3C4
  • UniProt Gene Name
    AR
  • UniProtKB
    P10275
Application Images
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  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
  • Xiang, F., Neal, P.: Efficient MCMC for temporal epidemics via parameter reduction. Comput. Stat. Data Anal.
For the process of attaching edges to nodes, it is straightforward to compute the likelihood using (2). However, because of the nature of weighted sampling without replacement, we have to, for each i, calculate the probability conditi onal on each of the Xi!Xi! permutations of the selected nodes and then aver age over all Xi!Xi! probabilities to arrive at the likelihood. As calculating the exact likelihood in this way is not computationally feasible because the factorial grows faster than the exponential function, we approximate the likelihood based on one permutation of weighted sampling without replacement instead. The contribution by the new edges brought by node i is
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