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The model assumes constant values for the many parameters used (listed in Table S2 of Text S1).
For PCR rounds #1 and #2 nested primers were used listed in Additional file 6.
Multiple machine learning approaches were used, listed in the Materials and Methods.
For the KD, the lentiviral mammalian vector pLKO.1 containing specific shRNAs (three shRNAs per target were used, listed below) along with the control shRNA (empty pLKO.1 plasmid) were obtained from Sigma.
The following antibody combinations were used (listed in the FITC/PE/PerCP/APC fluorochrome conjugate sequence): CD10/CD19/CD20/CD38, CD14/CD56/CD45/CD38, monoclonal kappa/monoclonal lambda/CD19/CD38, CD5/CD4/CD8/CD19, CD16/CD11b/CD45/CD34, intracellular monoclonal kappa/intracellular monoclonal lambda/CD45/CD38, intracellular polyclonal lambda/intracellular polyclonal kappa/CD45/CD38.
Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted using SYBR Premix Ex TaqTM (TaKaRa) in an Mx3000P QPCR system (Stratagene), with ACTIN used to as an internal control and the primers used listed in supplementary table S4, Supplementary Material online.
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All nodes were clustered automatically using listed KEGG pathway criteria and the MiMI plug-in.
We used lists of basic name segments (~3300), and stop words (~550).
We used lists collated by zip code.
To minimise bias, we used list-wise deletion in logistic regression analysis when data were missing.
Methods: We used lists of modified-release drug products to identify potential drug products.
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