# A Deep Representation Learning Method for Quantitative Immune Defense Function Evaluation and Its Clinical Applications

## TL;DR

This paper introduces ImmuDef, a new method using RNA-seq data and deep learning to quantify immune defense function and assess disease severity across multiple infections.

## Contribution

ImmuDef is the first quantitative framework for cross-disease immune defense evaluation using a VAE-based deep learning model.

## Key findings

- DImmuScore achieves 71.75%–76.25% accuracy in classifying immune states across 3202 samples.
- DImmuScore correlates with disease severity and can predict mortality in sepsis and COVID-19 patients.
- The framework is validated across five infectious diseases, establishing a quantitative standard for immune defense assessment.

## Abstract

The immune defense function protecting the body from invasive pathogens is a key indicator of an individual's health and lacks of methods for quantitative evaluation. This study introduces ImmuDef, a novel algorithm for precisely and quantitatively assessing anti‐infection immune defense function based on RNA‐seq data. ImmuDef selects immune signatures through comparisons of acquired immunodeficiency syndrome (AIDS) or severe sepsis vs. healthy controls (HC) and reduces dimension to construct a latent space via a variational autoencoder (VAE) model (QImmuDef‐VAE), a representation deep learning model. Based on this model, a defense immune score (DImmuScore) was calculated by measuring the distance between a patient and HC within latent space. We validated ImmuDef on 3202 samples across four immune states: immunodeficiency, immunocompromised, immunocompetent, and immunoactive. As a result, DImmuScore achieves high classification accuracy (mean accuracy: 71.75%–76.25%) among samples with various immune states and infections. Furthermore, DImmuScore can serve as a metric for infectious disease severity, where its gradient directly quantifies disease severity. As an application, DImmuScore can be a strong prognostic indicator, effectively stratifying mortality/survival in both sepsis and COVID‐19 patients with no symptomatic difference. This framework was validated across five infectious diseases, establishing the first quantitative standard for cross‐disease immune defense assessment.

ImmuDef, a novel algorithm to quantitatively evaluate the anti‐infection immune defense function of an individual based on RNA‐seq data via a variational autoencoder (VAE) model. It is validated on 3200+ samples across four immune states with high accuracy. It can serve as a metric for disease severity and prognosis across pathogenic cohorts. ImmuDef is the first quantitative method for cross‐disease immune defense assessment.