Pan-infection Foundation Framework Enables Multiple Pathogen Prediction

• DOI: 10.48550/arxiv.2501.01462
• Publication Date: 2024-12-31
• Authors: Zhang, Lingrui; Wu, Haonan; Jin, Nana; Zheng, Chenqing; Xie, Jize; Cai, Qitai; Wang, Jun; Cao, Qin; Zheng, Xubin; Wang, Jiankun; Cheng, Lixin
• Keywords: Genomics (q-bio.GN); FOS: Computer and information sciences; Computer Science - Machine Learning; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; FOS: Biological sciences; Quantitative Biology - Genomics; Machine Learning (cs.LG)

15 pages, 8 figures<br/>Host-response-based diagnostics can improve the accuracy of diagnosing bacterial and viral infections, thereby reducing inappropriate antibiotic prescriptions. However, the existing cohorts with limited sample size and coarse infections types are unable to support the exploration of an accurate and generalizable diagnostic model. Here, we curate the largest infection host-response transcriptome data, including 11,247 samples across 89 blood transcriptome datasets from 13 countries and 21 platforms. We build a diagnostic model for pathogen prediction starting from a pan-infection model as foundation (AUC = 0.97) based on the pan-infection dataset. Then, we utilize knowledge distillation to efficiently transfer the insights from this "teacher" model to four lightweight pathogen "student" models, i.e., staphylococcal infection (AUC = 0.99), streptococcal infection (AUC = 0.94), HIV infection (AUC = 0.93), and RSV infection (AUC = 0.94), as well as a sepsis "student" model (AUC = 0.99). The proposed knowledge distillation framework not only facilitates the diagnosis of pathogens using pan-infection data, but also enables an across-disease study from pan-infection to sepsis. Moreover, the framework enables high-degree lightweight design of diagnostic models, which is expected to be adaptively deployed in clinical settings.<br/>