Hands-on training in using large-scale multi-omics data and machine learning to infer biological models.
An effort to make malaria data available to health workers in Zambia has helped the country to reduce deaths from the disease by 92 percent in the southern region.
The 2018 conference offers 16 parallel tracks and 14 pre-conference workshops. Discussions include best practice case studies and joint partner presentations, with 280 industry and academic practitioners discussing themes of big data, smart data, cloud computing, trends in IT infrastructure, omics technologies, high-performance computing, data analytics, open source and precision medicine, from the research realm to the clinical arena.
Artificial intelligence could speed up metagenomic studies that look for species unknown to science.
The US National Institutes of Health's ‘All of Us Research Program’ seeks to provide new insights into who gets sick and why.
Smart technology and tracking tools make delivering flu resources and information a more current endeavor for public health officials.
Techniques in artificial intelligence and machine learning are dramatically altering biological research.
Open Source Intelligence (OSINT) and Signals Intelligence (SIGINT) from the clandestine intelligence sector are increasingly employed in infectious disease outbreaks.
A forecasting tool that uses data collected by state health departments could be used to forecast disease in the event of a bioterrorist attack or influenza pandemic.
The technology leverages advancements in artificial intelligence to distill complex data into a simple answer to identify flu viruses.