From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches
Year: 2023
Type: review
Abstract: A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.
Authors: Hanxi Xiao, Aaron Rosen, Prabal Chhibbar, Lenny Moise, Jishnu Das
Institution: University of Pittsburgh
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,175
Awards: Linking genome variation to transcriptional network dynamics in human B cells, Uncovering latent factors underlying weak and robust responses to influenza vaccine in healthy and obese older adults, Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in infectious disease
OpenAlex ID: W4389816366