Adrian Laurenzi

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Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial(More)
This paper reports on the design process of a web-based collaborative system for the production of multilingual health communication materials. The system is based on a workflow combining machine translation and human post-editing and has been designed for public health professionals who are bilingual domain experts but not necessarily trained translators.(More)
Over the past decade machine translation has reached a high level of maturity and is now routinely utilized by a wide variety of organizations, including multinational corporations, language service providers, and governmental/non-profit organizations. However, there are many communities that could benefit greatly from machine translation but do not(More)
This paper describes a novel collaborative machine translation (MT) plus post-editing system called PHAST (Public Health Automatic System for Translation,, tailored for use in producing multilingual education materials for public health. Its collaborative features highlight a new approach in public health informatics: sharing limited(More)
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