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content/23.sup.note.3.md

@@ -1,7 +1,7 @@
 ## Supplementary Note 3 - Implementation
 
 We build on recent technological and conceptual developments in biomedical ontologies that greatly facilitate the harmonisation of biomedical knowledge and advocate a philosophy of reuse of open-source software.
-For instance, we integrate a comprehensive “high-level” biomedical ontology, the Biolink model 1, which can be replaced or extended by more domain-specific ontologies as needed, and an extensive catalogue and resolver for biomedical identifier resources, the Bioregistry 3.
+For instance, we integrate a comprehensive “high-level” biomedical ontology, the Biolink model [@doi:10.1111/cts.13302], which can be replaced or extended by more domain-specific ontologies as needed, and an extensive catalogue and resolver for biomedical identifier resources, the Bioregistry [@doi:10.1038/s41597-022-01807-3].
 Both projects, like BioCypher, are open-source and community-driven.
 The ontologies serve as a framework for the representation of biomedical concepts; by supporting the Web Ontology Language (OWL), BioCypher allows integration and manipulation of most ontologies, including [those generated by Large Language Models](https://github.com/monarch-initiative/ontogpt).
 

content/25.sup.note.5.md

@@ -16,7 +16,7 @@ We have written such an adapter for UniProt data, using software infrastructure
 The adapter provides the data as well as convenient access points and an overview of the available property fields using Python Enum classes, offering automatic suggestion and autocomplete functionality.
 Using these methods, selecting specific content from the entirety of UniProt data and integrating this content with other resources is greatly facilitated (Figure @fig:S1), since the alternative would be, in many cases, to use a manual script to access the UniProt API and rely on manual harmonisation with other datasets.
 
-Similarly, we have added adapters for protein-protein interactions from the popular sources IntAct 7, BioGRID [@doi:10.1002/pro.3978], and STRING [@doi:10.1093/nar/gkaa1074], as well as other resources.
+Similarly, we have added adapters for protein-protein interactions from the popular sources IntAct [@doi:10.1093/nar/gkh052], BioGRID [@doi:10.1002/pro.3978], and STRING [@doi:10.1093/nar/gkaa1074], as well as other resources.
 For an up-to-date overview of the BioCypher pipelines and adapters, please visit the [Components board](https://github.com/orgs/biocypher/projects/3) and the [meta-graph](https://meta.biocypher.org).
 By using the UniProt accession of proteins in the KG and BioCypher functionality, the sources are seamlessly integrated into the final KG despite their differences in original data representation.
 As with UniProt data, access to interaction data is facilitated by provision of Enum classes for the various fields in the original data.