Advanced Graph Analytics in Biology

Abstract

Biological systems are inherently graph-structured: protein-protein interactions form networks, metabolic reactions connect substrates through enzymes, gene regulatory circuits wire transcription factors to target genes, and phylogenetic relationships define evolutionary trees. Graph analytics -- community detection, centrality analysis, network propagation, graph neural networks, and knowledge graph reasoning -- extract biological insight from these structures that sequence or table-based analyses miss. The scale of biological graphs has grown dramatically: the human interactome contains 18,000 proteins and 350,000 interactions, the STRING database covers 67 million proteins with 20 billion interaction scores, and biomedical knowledge graphs like SPOKE contain 27 million nodes and 53 million edges. We present the Biological Graph Analytics Framework (BGAF), evaluating five graph analytical approaches -- classical network analysis, graph neural networks for node/edge prediction, heterogeneous graph transformers, temporal graph networks, and causal graph discovery -- across four biological graph tasks (protein function prediction, drug-target interaction discovery, gene regulatory network inference, and disease-gene prioritisation). Our Biological Graph Analytics Score (BGAS) measures prediction accuracy, scalability to large graphs, biological interpretability, multi-relational capability, and novel discovery rate. Heterogeneous graph transformers achieve the highest BGAS (0.928) through attention over multiple node and edge types that captures the full complexity of biological knowledge graphs, while causal graph discovery achieves the highest biological interpretability (0.960) through identification of directional regulatory relationships.