4,532 research outputs found
Comparison of the CPU and memory performance of StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA)
High Energy Physics data sets are often characterized by a huge number of
events. Therefore, it is extremely important to use statistical packages able
to efficiently analyze these unprecedented amounts of data. We compare the
performance of the statistical packages StatPatternRecognition (SPR) and
Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory
usage of the learning process scale versus data set size. As classifiers, we
consider Random Forests, Boosted Decision Trees and Neural Networks. For our
tests, we employ a data set widely used in the machine learning community,
"Threenorm" data set, as well as data tailored for testing various edge cases.
For each data set, we constantly increase its size and check CPU time and
memory needed to build the classifiers implemented in SPR and TMVA. We show
that SPR is often significantly faster and consumes significantly less memory.
For example, the SPR implementation of Random Forest is by an order of
magnitude faster and consumes an order of magnitude less memory than TMVA on
Threenorm data
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Pluripotency factors functionally premark cell-type-restricted enhancers in ES cells.
Enhancers for embryonic stem (ES) cell-expressed genes and lineage-determining factors are characterized by conventional marks of enhancer activation in ES cells1-3, but it remains unclear whether enhancers destined to regulate cell-type-restricted transcription units might also have distinct signatures in ES cells. Here we show that cell-type-restricted enhancers are 'premarked' and activated as transcription units by the binding of one or two ES cell transcription factors, although they do not exhibit traditional enhancer epigenetic marks in ES cells, thus uncovering the initial temporal origins of cell-type-restricted enhancers. This premarking is required for future cell-type-restricted enhancer activity in the differentiated cells, with the strength of the ES cell signature being functionally important for the subsequent robustness of cell-type-restricted enhancer activation. We have experimentally validated this model in macrophage-restricted enhancers and neural precursor cell (NPC)-restricted enhancers using ES cell-derived macrophages or NPCs, edited to contain specific ES cell transcription factor motif deletions. DNA hydroxyl-methylation of enhancers in ES cells, determined by ES cell transcription factors, may serve as a potential molecular memory for subsequent enhancer activation in mature macrophages. These findings suggest that the massive repertoire of cell-type-restricted enhancers are essentially hierarchically and obligatorily premarked by binding of a defining ES cell transcription factor in ES cells, dictating the robustness of enhancer activation in mature cells
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