From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

ABSTRACTA 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...

Full description

Saved in:
Bibliographic Details
Main Authors: Hanxi Xiao (Author), Aaron Rosen (Author), Prabal Chhibbar (Author), Lenny Moise (Author), Jishnu Das (Author)
Format: Book
Published: Taylor & Francis Group, 2023-12-01T00:00:00Z.
Subjects:
Online Access:Connect to this object online.
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:ABSTRACTA 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.
Item Description:10.1080/21645515.2023.2282803
2164-554X
2164-5515