Network-Driven Drug Discovery.

We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and experimental approach is formulated on a conceptual framework in which network biology is used to bridge between individual molecular entities and the cellular phenotype that emerges when those entities interact in a network. Multiple aspects of early stage discovery are addressed including the data-driven elucidation of biological processes implicated in disease, target identification and validation, phenotypic discovery of active molecules and their mechanism of action, and extraction of genetic target support from human population genetics data. Validation is described via summary of a number of discovery projects and details from a project aimed at COVID-19 disease.

Measuring rank robustness in scored protein interaction networks.

BACKGROUND: Protein interaction databases often provide confidence scores for each recorded interaction based on the available experimental evidence. Protein interaction networks (PINs) are then built by thresholding on these scores, so that only interactions of sufficiently high quality are included. These networks are used to identify biologically relevant motifs or nodes using metrics such as degree or betweenness centrality. This type of analysis can be sensitive to the choice of threshold. If a node metric is to be useful for extracting biological signal, it should induce similar node rankings across PINs obtained at different reasonable confidence score thresholds. RESULTS: We propose three measures-rank continuity, identifiability, and instability-to evaluate how robust a node metric is to changes in the score threshold. We apply our measures to twenty-five metrics and identify four as the most robust: the number of edges in the step-1 ego network, as well as the leave-one-out differences in average redundancy, average number of edges in the step-1 ego network, and natural connectivity. Our measures show good agreement across PINs from different species and data sources. Analysis of synthetically generated scored networks shows that robustness results are context-specific, and depend both on network topology and on how scores are placed across network edges. CONCLUSION: Due to the uncertainty associated with protein interaction detection, and therefore network structure, for PIN analysis to be reproducible, it should yield similar results across different confidence score thresholds. We demonstrate that while certain node metrics are robust with respect to threshold choice, this is not always the case. Promisingly, our results suggest that there are some metrics that are robust across networks constructed from different databases, and different scoring procedures.