Optimizing transformer-based prediction of human microbe-disease associations through integrated loss strategies.

ABSTRACT: Microorganisms play an important role in many complex diseases, influencing their onset, progression, and potential treatment outcomes. Exploring the associations between microbes and human diseases can deepen our understanding of disease mechanisms and assist in improving diagnosis and therapy. However, traditional biological experiments used to uncover such relationships often demand substantial time and resources. In response to these limitations, computational methods have gained traction as more practical tools for predicting microbe-disease associations. Despite their growing use, many of these models still face challenges in terms of accuracy, stability, and adaptability to noisy or sparse data. To overcome the aforementioned limitations, we propose a novel predictive framework, Hyp

SUBMITTER: Zhu R 

PROVIDER: S-EPMC12453706 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature