187,342 research outputs found
Spellman (John W.) Political Theory of Ancient India
No full textVernant Jean-Pierre. Spellman (John W.) Political Theory of Ancient India. In: Archives de sociologie des religions, n°18, 1964. p. 214
Expression profile for STE12 for time series data from Spellman et al. [32].
No full text<p>Expression profile for STE12 for time series data from Spellman et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006633#pone.0006633-Spellman1" target="_blank">[32]</a>.</p
Expression profile for RPH1 for time series data from Spellman et al. [32].
No full text<p>Expression profile for RPH1 for time series data from Spellman et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006633#pone.0006633-Spellman1" target="_blank">[32]</a>.</p
Expression profile for SRD1 for time series data from Spellman et al. [32].
No full text<p>Expression profile for SRD1 for time series data from Spellman et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006633#pone.0006633-Spellman1" target="_blank">[32]</a>.</p
Expression profile for ADR1 for time series data from Spellman et al. [32].
No full text<p>Expression profile for ADR1 for time series data from Spellman et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006633#pone.0006633-Spellman1" target="_blank">[32]</a>.</p
Expression profile for PIP2 for time series data from Spellman et al. [32].
No full text<p>Expression profile for PIP2 for time series data from Spellman et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006633#pone.0006633-Spellman1" target="_blank">[32]</a>.</p
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Average rMSE/Mean on the Spellman dataset for gene CLN2.
No full text<p>Twenty inference runs have been performed with datasets Hasse, PramilaL and Hasse+PramilaL combined, and average errors, tested on the Spellman dataset, displayed for each normalisation technique. These show that, for PMOnly and LoessOnly methods, behaviour on the test dataset improves when using combined data, regardless of the cross-platform normalisation technique used, while for PMLoess methods this happens only for ComBat cross-platform normalisation. This is a good indication that these within dataset normalisation methods improve integrated data inference. Loess_XPN displays lowest rMSE values, suggesting that this is a suitable normalisation method for cross-platform data integration.</p
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