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• W4212772904 abstract "Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms." @default.
• W4212772904 created "2022-02-24" @default.
• W4212772904 creator A5041796489 @default.
• W4212772904 creator A5042789303 @default.
• W4212772904 date "2022-02-14" @default.
• W4212772904 modified "2023-09-27" @default.
• W4212772904 title "Gaining confidence in inferred networks" @default.
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• W4212772904 doi "https://doi.org/10.1038/s41598-022-05402-9" @default.
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