BioDecoder: A miRNA Bio-interpretable Neural Network Model for Noninvasive Diagnosis of Breast Cancer

Early diagnosis of breast cancer remains a major clinical challenge. Liquid biopsy has become a powerful tool for cancer diagnosis by the aid of various the state-of-the-art detection technologies and artificial intelligence (AI) methods. Although the prediction performance is superior, the clinical application of existing AI models is greatly limited due to their poor interpretability. Here, we designed a miRNA-Gene-Module-Pathway-Disease biological decoding path, and developed BioDecoder thereof, a miRNA bio-interpretable neural network model for breast cancer early diagnosis. We demonstrated that BioDecoder could achieve early non-invasive diagnosis of breast cancer with a remarkable performance (AUC = 0.989) and showed strong generalizability in an external cohort (AUC = 0.890). Meanwhile, the biologically interpretable results of BioDecoder revealed that significant changes in metabolic pathway and oxidative phosphorylation were the main action pathways of circulating miRNA in breast cancer. Our study indicates that BioDecoder offers the promise of non-invasive early diagnosis of breast cancer and can be generalized to other cancers and corresponding biomarkers.