FR-BINN: Biologically Informed Neural Networks for Enhanced Biomarker Discovery and Pathway Analysis

Abstract

Chronic inflammation plays a pivotal role in human health, with certain inflammatory conditions significantly increasing the risk of cancer, while others do not. However, the molecular mechanisms underlying this divergent risk remain poorly understood. In this study, we propose FR-BINN, a biologically informed neural network framework for disease prediction and interpretability. Incorporating Fenton reaction (FR)-related biological priors and leveraging multiple interpretability methods, FR-BINN identifies key genes driving cancer-prone and non-cancer-prone chronic inflammatory diseases. The experimental results demonstrate that FR-BINN achieves superior classification performance while offering biologically interpretable insights. Moreover, attribution results derived from different explainable techniques show high consistency, and intra-method results exhibit distinct patterns across disease categories. We further combine large language models with feature attributions to identify candidate biomarkers, and independent datasets confirm the robustness of these findings. Notably, genes such as NCOA1 and SDHB are identified as being associated with cancer susceptibility. The framework further reveals distinct patterns in energy metabolism, oxidative stress, and pH regulation between cancer-prone and non-cancer-prone inflammatory diseases. These insights enhance our understanding of inflammation-associated tumorigenesis and contribute to the identification of potential biomarkers and therapeutic targets.