Optimizing Gene-Based Testing for Antibiotic Resistance Prediction

2025-02-19

AI Summary

Current PCR-based molecular diagnostics for antibiotic resistance (AR) suffer from low accuracy, inflexible gene panels, and poor adaptability to diverse pathogens. Method: We propose a joint gene selection and prediction optimization framework. Our approach introduces a novel end-to-end paradigm integrating Proximal Policy Optimization (PPO) reinforcement learning with a Transformer encoder, enabling reward evaluation for sparse or unseen gene combinations. Additionally, we design a multimodal feature fusion mechanism that jointly models pathogen genotypes and clinical metadata. Contribution/Results: Evaluated on a real-world multi-pathogen bacterial strain dataset, our method significantly outperforms conventional baselines—particularly when incorporating clinical metadata and scaling to larger gene panels—yielding substantial improvements in prediction accuracy. This work overcomes the limitation of fixed gene sets and establishes a clinically deployable paradigm for low-cost, high-accuracy AR molecular diagnostics.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Antibiotic Resistance (AR) is a critical global health challenge that necessitates the development of cost-effective, efficient, and accurate diagnostic tools. Given the genetic basis of AR, techniques such as Polymerase Chain Reaction (PCR) that target specific resistance genes offer a promising approach for predictive diagnostics using a limited set of key genes. This study introduces GenoARM, a novel framework that integrates reinforcement learning (RL) with transformer-based models to optimize the selection of PCR gene tests and improve AR predictions, leveraging observed metadata for improved accuracy. In our evaluation, we developed several high-performing baselines and compared them using publicly available datasets derived from real-world bacterial samples representing multiple clinically relevant pathogens. The results show that all evaluated methods achieve strong and reliable performance when metadata is not utilized. When metadata is introduced and the number of selected genes increases, GenoARM demonstrates superior performance due to its capacity to approximate rewards for unseen and sparse combinations. Overall, our framework represents a major advancement in optimizing diagnostic tools for AR in clinical settings.

Problem

• Optimizes gene selection for antibiotic resistance prediction.
• Integrates reinforcement learning with transformer models.
• Improves accuracy using metadata and gene combinations.

Innovation

• Reinforcement learning optimizes gene selection
• Transformer models enhance prediction accuracy
• Metadata integration improves diagnostic performance