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A team of researchers led by Yale School of Medicine’s Steven Kleinstein, PhD, is striving to understand why some people’s immune systems generate a robust protective response post-vaccination while others’ fail, and how this differs across vaccines. The team has published a series of new papers on its investigations.

Yale researchers have found a new use for blockchain. They’ve adapted the technology to give individuals control of their own genomes.

While other, smaller studies have uncovered invaluable data about important aspects of the virus and the pandemic, this research and the resources that have gone into it promise to produce the most enlightening insights yet.

Researchers Connect Spinal Fluid Autoantibodies to Neurological Symptoms in COVID-19 Patients

Yale a Site for New COVID-19 NIAID Study

Exome sequencing allows scientists and clinicians to zero in on the mutations responsible for a disparate array of ailments.

Cutting-edge tools and analyses are digging deeper than ever before to unveil the intricacies of the diverse human immune system. Vaccines save lives — but they don’t always work. Take the annual influenza shot: by some estimates, flu vaccines are only 50–70% effective even when well matched to the virus strains in broad circulation. Despite all the research, scientists still cannot predict whether a given vaccine will work for any specific person.

ImmuNet is a new online tool that predicts the role of key proteins and genes in diseases of the human immune system. Details of the publically available resource were the cover story in the September 15, 2015 issue of the journal Immunity. The tool uses information compiled from 38,088 public experiments to predict new immune pathway interactions, mechanisms, and disease-associated genes. With advances in inexpensive computing power, and stored data collections becoming massive in the era of “big data,” researchers are now able to combine algorithms and models into tools like ImmuNet that pull previously unrecognized disease patterns from databases. These computational patterns are predictive, and researchers can test them with further experiments.