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The experimentation using real time datasets illustrates the efficacy of the proposed algorithm.
The difference, arising from the two IWC datasets, illustrates the significant influence of the observation uncertainties.
The computational time required for some of these other datasets illustrates the computational cost for a wide variety of scenarios.
Application of these gene sets to analysis of preexisting datasets, illustrates a strong link between bradyzoite regulation and cell cycle and provides additional insight into the hierarchy of genes that lead to developmental transitions in T. gondii.
The analysis of three herd-level datasets illustrates the value of using WGS data for the study of epidemiologically linked isolates and that VNTR typing data may lead to an incorrect assessment of diversity and relatedness of strains.
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Results on crowdsourced datasets illustrate the efficiency and effectiveness of our proposed Bag-of-Iconics representation.
Extensive experiments on real-world datasets illustrate that our DP-MFSM algorithm can substantially outperform alternative approaches.
Extensive experiments on real-world datasets illustrate that DP-Apriori substantially outperforms the state-of-the-art techniques.
The proposed system is demonstrated on a pair of hydrographic datasets, illustrating the adaptation of the algorithm to different depth- and sensor-driven data densities.
The confidence intervals obtained for the test datasets illustrate these differences, but no systematic comparison of the confidence intervals obtained with the different tools was made.
Our detailed experimental evaluation using both real and synthetic datasets illustrate that our approach improves the performance of the workloads as much as 50%, compared to its competitors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com