Vincent A. Fusaro

TL;DR: Results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.

TL;DR: The intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems.

TL;DR: The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems, and the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost.

TL;DR: The results indicate that it is possible to achieve high classification accuracy, sensitivity, and specificity as well as clinically acceptable inter-rater reliability with nonclinical personnel and further suggests that at least a percentage of the effort associated with detection and monitoring of autism may be mobilized and moved outside of traditional clinical environments.

TL;DR: A model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted is created and found the optimal cluster size to minimize waste and maximize usage.