Using Molecular Networks to Seed Predictive Hierarchies of Cell Systems

on YouTube ↗01h 11m

" summary

Builds a data-driven hierarchy from integrated yeast networks (protein, genetic, co-expression) using CLIXO/clique-extracted ontologies and ontology alignment to GO, recapitulating many GO terms (about 60% coverage of cell component) and recovering proteasome substructure while flagging novel terms (e.g., Term 183). Adds targeted genetic interaction screens for autophagy to fill ~70% missing space and raises GO correlation from ~30% to ~50%. Functionalizes hierarchies by converting genotypes to ontotypes→random forest models to predict pairwise and some three-way phenotypes (r ≈ 0.3).

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" chapters (11)

1. 0:00Intro
2. 1:07Two faces of a common challenge
3. 10:45Using network data to assemble hierarchical models of cellular subsystems (Nexo/Clixo approach)
4. 13:57Example: the hierarchy of core autophagy
5. 22:33Integrating data to build hierarchical models of a process (e.g. autophagy) or the cell
6. 25:33Selecting the next type of experiment
7. 29:23Recovery of GO, new vs previous data
8. 37:17Challenge 2
9. 37:33Moving from maps to predictive models Siri and other agents associate ontology terms with states to interpret the query
10. 45:21Gene ontologies encode functional logic of genetic interactions
11. 53:04Prediction of three-way interactions

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