The probability of edge existence due to node degree: a baseline for network-based predictions.

ABSTRACT: Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than de

SUBMITTER: Zietz M 

PROVIDER: S-EPMC9881952 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature