Background Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.
Psychology is one of the most recent sciences to issue from the mother-tree of philosophy. One of the greatest challenges is that of formulating theories and methodologies that move the field toward theoretical structures that are not only sufficient to explain and predict phenomena but, in some vital sense, necessary for those purposes. Mathematical modeling is perhaps the most promising general strategy, but even under that aegis, the physical sciences have labored toward that end. The present chapter begins by outlining the roots of our approach in 19th century physics, physiology, and psychology. Then, we witness the renaissance of goals in the 1960s, which were envisioned but not usually realizable in 19th century science and methodology. It could be contended that it is impossible to know the full story of what can be learned through scientific method in the absence of what cannot be known. This precept brings us into the slough of model mimicry, wherein even diametrically opposed physical or psychological concepts can be mathematically equivalent within specified observational theatres! Discussion of examples from close to half a century of research illustrate what we conceive of as unfortunate missteps from the psychological literature as well as what has been learned through careful application of the attendant principles. We conclude with a statement concerning ongoing expansion of our body of approaches and what we might expect in the future.
Automation can be unreliable. This makes appropriate trust and reliance difficult to calibrate. One solution to building appropriate trust is to increase automation transparency by displaying information to the operator about the technology’s underlying analytical principles. However, displaying this additional information may increase operator workload. The research and development community must balance the competing demands of providing adequate transparency and keeping operator workload low. To investigate the complex effects of transparency on workload, a modeling approach can be used by computing a measure of processing efficiency called the capacity coefficient. We conducted a study to examine the impact of increasing transparency on operator workload using the capacity coefficient. We present the data from one participant with the goal of demonstrating the utility of the capacity coefficient. We discuss how this participant’s data highlights the inferences possible from capacity analysis for measuring the impact of display design and increased transparency on operator workload.
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