| | 35 | |
| | 36 | == (Notes) == |
| | 37 | * look into data |
| | 38 | * cross links —> protein underlying peaks ? |
| | 39 | * biobanks : phenotypic information e.g lifelines project data : annotate question : ARE there other data set in the world? —> merge into lifelines data … |
| | 40 | * next step : come up with an "algorithm" that does the mapping . Let's assume we have 2 studies , we would like to merge and export the results . |
| | 41 | * it's not really an algorithm , but more of a "correspondence " rule …If we have 2 questions - "Are they compatible "? or if not what kind of conversion should be done in order to match each other? So then we'll have a meta study ..for each biobank —> mapping |
| | 42 | * So we have available 5 biobanks —> project on a single parameter —> bigger statistical analysis . |
| | 43 | * How to model it ? |
| | 44 | * RDF rules? |
| | 45 | * parameter in one biobank / corresponding parameter in the other biobank ? |
| | 46 | * a potential pilot would be like to |
| | 47 | |
| | 48 | 1. take 2 pheno DBs , |
| | 49 | 1. fill with lifelines data , |
| | 50 | 1. query that merges the set —> maybe a sparql query ? |
| | 51 | 1. different question |
