Grand Challenges in Computational Biology - Project web sites - Inria

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Automatic protein function predictionusing a hyper-dimensional network
Hyperdimensional information networkfor data integration, navigation & community annotationNodes: Genes/proteinsEdges: different types of connection between genes (e.g., orthology, similarstructure, interaction, disease association, regulated by, adjacent in metabolicnetwork, genome neighbor, etc.).Edges have weights proportional to confidenceExperimental data can enter at any point in the graph, and be propagated toneighboring nodes based on learned rules:• Biological process for one gene can be made available to genome neighbors• A protein-protein interaction between two genes in one species can be used to infer correspondinginteraction between their orthologs in another• Roles in a pathway (e.g., EC number) known for one gene can be assigned to an ortholog• Participation in a biological process can be inferred based on genome neighbors• 3D structure information can be propagated to all homologs• Protein structure information can be propagated to all homologsBiologists can: subscribe to news feeds arriving at their selected nodes, upload data, attach links totheir papers, manually curate biological functionsManual annotations from biologists will need to be weighted according to estimated confidence
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Page 3 and 4: Increasing the specificity of funct
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Page 9 and 10: SNP prioritization and interpreting
Page 11: Was there really life before the we

Grand Challenges in Computational Biology - Project web sites - Inria

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