Adrian Laurenzi

Learn More
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, phastsystem.org), 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)
  • 1