Business Context
In bioinformatics, researchers are analyzing a protein-protein interaction network where each link has a 'confidence score' (weight). They need to identify functional modules (cliques) but only those where the sum of interaction confidence scores exceeds a high threshold, filtering out weak or noise-based associations.
About the Set : optNetwork
Network analysis and graph algorithms.
Discover all actions of optNetwork Data Preparation
Creation of a protein interaction network with weights. P1-P2-P3 form a clique with high weights, while P4-P5-P6 form a clique with low weights.
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| 1 | |
| 2 | DATA mycas.protein_interactions; |
| 3 | INPUT prot_a $ prot_b $ conf_score; |
| 4 | DATALINES; |
| 5 | P1 P2 5.0 P1 P3 5.0 P2 P3 5.0 P4 P5 1.0 P4 P6 1.0 P5 P6 1.0; |
| 6 | |
| 7 | RUN; |
| 8 |
Étapes de réalisation
1
Execute clique action using 'minLinkWeight' to isolate only high-confidence protein complexes.
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| 1 | |
| 2 | PROC CAS; |
| 3 | ACTION optNetwork.clique / links={name='protein_interactions', vars={from='prot_a', to='prot_b', weight='conf_score'}} minLinkWeight=12.0 out={name='high_conf_complexes', replace=true}; |
| 4 | |
| 5 | RUN; |
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Expected Result
The output table 'high_conf_complexes' should ONLY contain the clique {P1, P2, P3} (Total Weight = 15.0). The clique {P4, P5, P6} (Total Weight = 3.0) must be excluded because it does not meet the minLinkWeight threshold of 12.0.