RT Journal Article
SR Electronic
T1 The probability of edge existence due to node degree: a baseline for network-based predictions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.01.05.522939
DO 10.1101/2023.01.05.522939
A1 Zietz, Michael
A1 Himmelstein, Daniel S.
A1 Kloster, Kyle
A1 Williams, Christopher
A1 Nagle, Michael W.
A1 Greene, Casey S.
YR 2023
UL http://biorxiv.org/content/early/2023/01/06/2023.01.05.522939.abstract
AB 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 degree is often only a small portion of overall performance. Degree’s predictive performance diminishes when the networks used for training and testing—despite measuring the same biological relationships—were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).Competing Interest StatementThis work was funded, in part, by Pfizer Worldwide Research, Development, and Medical.