# Artificial intelligence–driven analysis of antibody and nucleic acid biomarkers for enhanced disease diagnostics

## TL;DR

This paper introduces a new AI framework combining biological knowledge with machine learning to improve disease diagnostics using antibody and nucleic acid biomarkers.

## Contribution

The novel framework, BioGraphAI with ACKR, integrates biological priors and semi-supervised learning to enhance biomarker diagnostics.

## Key findings

- BioGraphAI uses hierarchical graph attention to model multi-modal biological interactions.
- ACKR improves model reliability through biomedical knowledge regularization.
- The framework supports interpretable and generalizable disease diagnostics from heterogeneous data.

## Abstract

The rapid evolution of artificial intelligence (AI) technologies has catalyzed a paradigm shift in the landscape of biomarker-driven disease diagnostics, particularly in the context of integrating antibody and nucleic acid indicators. Within this transformative setting, AI offers unprecedented potential for decoding complex molecular interactions across heterogeneous data sources, facilitating early and precise disease identification. However, the effective deployment of AI in this domain mandates enhanced model interpretability, robust cross-domain generalization, and biologically grounded learning strategies—challenges that resonate deeply with contemporary research focused on antibody and nucleic acid diagnostics.

Traditional methodologies for biomarker discovery—such as linear regression, random forests, and even standard deep neural networks—struggle to accommodate the multi-scale dependencies and missingness typical of omics datasets. These models often lack the structural alignment with biological processes, resulting in limited translational utility and poor generalization to new biomedical contexts. To address these limitations, we propose a novel framework that integrates a biologically informed architecture, BioGraphAI, and a semi-supervised learning strategy, adaptive contextual knowledge regularization (ACKR). BioGraphAI employs a hierarchical graph attention mechanism tailored to capture interactions across genomic, transcriptomic, and proteomic modalities. These interactions are guided by biological priors derived from curated pathway databases.

This architecture not only supports cross-modal data fusion under incomplete observations but also promotes interpretability via structured attention and pathway-level embeddings. ACKR complements this model by incorporating weak supervision signals from large-scale biomedical corpora and structured ontologies, ensuring biological plausibility through latent space regularization and group-wise consistency constraints.

Together, BioGraphAI and ACKR represent a step toward overcoming critical barriers in biomarker-driven disease diagnostics. By grounding computational predictions in biological priors and enhancing interpretability through structured embeddings, this framework advances the translational applicability of AI for early and precise disease identification.