Connectivity is a fundamental property of biological systems: on the cellular level, proteins interact with each other to form protein-protein interaction networks; on the organism level, neurons are arranged in a network; and on a community level, species can have complex relationships with one another that drive the development of an ecosystem. Graphs, mathematical representations modeling entities as vertices and their relationships as edges, have proved useful for understanding biological systems that naturally have such a network structure. Graph representations and algorithms (often in combination with machine learning techniques) can be used to organize massive amounts of related (and sometimes heterogenous or unstructured) data, and to ultimately to identify patterns that represent novel biological insights. Additionally, recent advances in high order networks, hypergraphs, and computational topology promise to bring a higher level of complexity to network models. This PSB session ``Graph Representations and Algorithms in Biomedicine,'' will encompass modern developments in graph theory, computational topology, and graph machine learning applied to various fields of biomedicine.

We invite submissions that aim to advance biomedicine by constructing, comparing, and analyzing graphs and hypergraphs. Broadly, this may include:

• Algorithms for assembling graphs from large datasets and available metadata

• Graph algorithms for processing heterogeneous and unstructured datasets

• Novel machine learning techniques for node, link, and graph prediction tasks

• Generative models for constructing graphs

• Applications of graphs to drug design, precision health, clinical practice, and biomedical ontologies

• Advancing fairness and interpretability of graph-based algorithms in biomedicine

Specific topics of interest include:

• Longitudinal graph-based analysis for temporal data

• Hybrid graph and genome-based approaches for population genetics

• Biomedical knowledge graph construction from large and heterogeneous corpora of data (e.g. multi-omics, EHR, biomedical text, etc.)

• Graph representation learning algorithms for generating node embeddings

• Interpretability of graph neural networks

• Measurements and methods for ensuring fairness of graph algorithms

• Visualization techniques for graph algorithms