A novel deep learning method using contrastive learning enables interpretable outputs from relationships between gene expression and histone modification

Abstract

Current methods for predicting gene expression from histone modifications rely on arbitrary binary classification thresholds such as the median to distinguish between high and low expression for genes. This approach lacks biological justification, creates dataset-dependent classifications, and ignores the relative regulatory relationships between genes that are often more biologically meaningful than absolute cutoffs. We introduce a novel pairwise ranking approach that compares relative expression levels between gene pairs based on their histone modification patterns, eliminating arbitrary threshold selection. Evaluation using the REMC E066 and GSE76344 datasets showed that our pairwise classification framework demonstrated consistent ranking performance in both datasets. The ablation study found that only a subset of histone modification types is necessary, giving evidence of redundancy in the biological code.