apply some Gigascience copyeditor changes (#75)

merges #75 from #69 (comment) Co-authored-by: Michael Zietz <[email protected]>
greenelab · Jan 12, 2024 · 084aa98 · 084aa98
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diff --git a/content/01.abstract.md b/content/01.abstract.md
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 ## Abstract {.page_break_before}
 
-Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction.
+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.