Experimental and analytical advances have enabled systematic, high-resolution studies of humoral immune responses, and are beginning to define mechanisms of immunity to HIV.
High-throughput, information-rich experimental and analytical methods, whether genomic, proteomic, or transcriptomic, have firmly established their value across a diversity of fields. Consideration of these tools as trawlers in ‘fishing expeditions’ has faded as ‘data-driven discovery’ has come to be valued as an irreplaceable means to develop fundamental understanding of biological systems. Collectively, studies of HIV-1 infection and vaccination including functional, biophysical, and biochemical humoral profiling approaches have provided insights into the phenotypic characteristics of individual and pools of antibodies. Relating these measures to clinical status, protection/efficacy outcomes, and cellular profiling data using machine learning has offered the possibility of identifying unanticipated mechanisms of action and gaining insights into fundamental immunological processes that might otherwise be difficult to decipher.
Recent evidence establishes that systematic data collection and application of machine learning approaches can identify humoral immune correlates that are generalizable across distinct HIV-1 immunogens and vaccine regimens and translatable between model organisms and the clinic. These outcomes provide a strong rationale supporting the utility and further expansion of these approaches both in support of vaccine development and more broadly in defining mechanisms of immunity.
aDepartment of Computer Science
bMolecular and Cell Biology Program
cThayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
Correspondence to Margaret E. Ackerman, 14 Engineering Dr, Hanover, NH 03755, USA. Tel: +1 603 646 9922; fax: +1 603 646 3856; e-mail: email@example.com