Functional prediction of DNA/RNA-binding proteins by deep learning from gene expression correlations

Osato, N.

doi:10.1101/2025.03.03.641203

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bioRxiv · 10.1101/2025.03.03.641203

Functional prediction of DNA/RNA-binding proteins by deep learning from gene expression correlations

Osato, N.

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Abstract

Understanding how regulatory architectures are reorganized during biological state transitions remains a central challenge in functional genomics. Here, we integrate co-expression-derived regulatory interactions with interpretable deep learning to compute gene-level contribution scores and introduce {Delta}NES (normalized enrichment score difference) to quantify pathway redistribution across biological states. Applying this framework to neural progenitor and leukemia-associated cellular states, we identify systematic redistribution of functional modules across RNA-binding proteins, including PKM, HNRNPK, and NELFE. Neural System- and Immune System-associated modules are differentially positioned along contribution-ranked regulatory landscapes, while Signal Transduction consistently forms a conserved signaling backbone. These findings suggest that pathway redistribution reflects regulatory reorganization across biological states and provides a framework for interpreting large-scale regulatory coordination beyond expression-centric analyses.

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Osato, N.. 2025-03-10. Functional prediction of DNA/RNA-binding proteins by deep learning from gene expression correlations. https://doi.org/10.1101/2025.03.03.641203

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