In-silico generation of high-dimensional immune response data in patients using a deep neural network.

ABSTRACT: Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cel

SUBMITTER: Fallahzadeh R 

PROVIDER: S-EPMC10182197 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature