BioLM-NET: an interpretable deep learning model combining prior biological knowledge and contextual LLM gene embeddings on multi-omics data to predict disease

Biologically informed deep neural networks, which connect input layer to hidden layers based on genepathway relationship have gained popularity in recent years. However, most existing methods do not incorporate protein-protein interactions (PPI) and protein-DNA interactions (PDI) in their designs. In this study, we introduce BioLM-NET, a deep learning-based framework that fuses single cell or bulk gene expression data and DNA methylation data with prior biological knowledge including Protein- Protein Interactions (PPI), Protein-DNA Interactions (PDI). BioLM-NET also aggregates latent representation of omics signals at pathway-level through an attention-based pathway layer where a pretrained large language model (LLM) was incorporated to generate context-specific gene embeddings. We evaluated BioLM-NET on single cell colorectal cancer data from scTrioseq2 platform to predict primary and metastatic cancer cells, on TCGA-BRCA, TCGA-GBM, TCGA-COAD to predict cancer subtypes and ROSMAP data to predict Alzheimer's disease patient. Our results showed that BioLMNET outperformed baseline and state-of-the-art (SOTA) methods, P-NET and PASNet with statistical significance on scTrioseq2 data, TCGA-COAD and ROSMAP data and ties with SVM and Dense neural network on TCGA-BRCA data. Our ablation studies demonstrated the importance of incorporating PPI, PDI data and attention-based pathway layer. We also interpret our models and found out that our important input features are significantly enriched in GO terms and KEGG pathways and can serve as potential biomarkers or therapeutic targets for the corresponding disease.