Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions

摘要: Background To grasp the complexity of biological processes, knowledge is often translated into schematic diagrams pathways, such as signalling and metabolic pathways. These pathway describe relevant connections between entities incorporate domain in a visual format that easier for humans to interpret. It has already been established these can be represented machine readable formats, done KEGG, Reactome, WikiPathways. However, while are good at interpreting message creator diagram, algorithms struggle when diversity drawing approaches increases. WikiPathways supports multiple styles, therefore needs harmonize this offer semantically enriched access via Resource Description Framework format. Particularly challenging normalization interactions entities, so we glean information about connectivity represented. include type interaction (metabolic conversion, inhibition, etc.), direction, participants. Availability semantic harmonized enables searching full network integration with linked data cloud. Results We here study how graphically modelled semantified efficiently, exemplify resulting used programmatically answer questions. We find translate sufficient degree model discuss some current limitations. Furthermore, show base specific questions. Conclusion This paper demonstrates most graphical from layer intact preserved. The usability shown useful integrated other platforms. Being able evaluate elements affect each allows, example, identification up or downstream targets will have similar effect modified.