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An algorithm to select between several deterministic or stochastic dynamical models for a given dataset has been implemented; here the model identifiers/labels are treated as an additional parameter, as described in Toni and Stumpf, 2010.
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After learning from recent years' works in this field and analysing a large dataset, NERVE has been implemented and tuned as the first available tool able to rank a restricted pool (~8 9% of the whole proteome) of vaccine candidates and to show high recall (~75 80%) of known protective antigens.
BFAST has been implemented completely for the handling of large datasets from the ABI SOLiD color space reads as well as direct sequence reads possible with other platforms.
The method has been implemented as software for analysis of potential splicing variants from large datasets and catalogues their effects.
However, a more stringent PCC threshold was avoided, as further filtering has been implemented by selecting gene pairs that were correlated across all three datasets.
Not a single proposal has been implemented.
To carry it out has been implemented within the software SHEE (Simulation of Hydrological Extreme Events), which is a tool for the analysis of hydrological processes in catchments with the management and display of DEM and datasets.
The approach has been implemented as a set of plug-ins within the open source process mining framework ProM and has been evaluated using two real-life datasets from the insurance domain to assess the usefulness of such a tool.
This change has been implemented.
MP-Align has been implemented in Python.
This technique has been implemented in FastCodeML.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com