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Leverage Organic Intelligence

Only a trained biologist can understand the semantic context of the experiment as well as the semantic meaning of enriched meta-data (annotation, signaling pathways, etc.) and gene-gene relationships (protein-protein interactions, literature co-occurrence, etc); and that biologist will have to make a significant commitment of time and/or funds to carry out follow-up experiments.

Automated statistical methods and machine learning algorithms can only identify patterns in data. Novel insight into Systems Biology can therefore only be produced by combining machine-generated suggestions with the knowledge and intuition of an expert biologist.

EGAN has been designed to put the organic intelligence of the biologist in control of the exploratory analysis process by:

  • Eliminating the requirement for the biologist to write programs or run software to download gene annotation and network information (this can be a huge, frustrating time sink)
  • Streamlining the user interface to provide consistent functionality without requiring use of differentially supported and documented plug-ins
  • Providing the biologist with familiar searchable/sortable tables for identifying interesting genes and gene sets
  • Provoking a comprehensive rational interpretation of assay results via hypergraph visualization
  • Directly linking the user to over 100k web page entries for genes and gene sets (NCBI Entrez Gene, Gene Ontology, KEGG, etc.) and over 300k context-specific articles at PubMed; but not all at once; the key is to provide only the few, most pertinent links (numbers are for H. sapiens)
  • Establishing a biologist-centric paradigm for repeated investigation of multiple exploratory analysis results from diverse high-throughput -omics technologies
This EGAN-produced image shows a set of genes found to be up-regulated and have increased copy in breast cancer patients with poorer survival, connected by literature, chromosomal adjacency and enriched gene sets: KEGG, Cytoband, and Gene Ontology. These genes and terms/pathways are potential drivers of the poor survival phenotype.
This EGAN-produced image shows a set of genes found to be up-regulated and have increased copy in breast cancer patients with poorer survival, connected by literature (orange), chromosomal adjacency (gray) and enriched gene sets: KEGG (blue), Cytoband (purple) and Gene Ontology (yellow). These genes and terms/pathways are potential drivers of the poor survival phenotype.

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