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Fig. 6 | BioData Mining

Fig. 6

From: A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism

Fig. 6

Simplified depiction of maximum flow formulation. (Upper) This is the flow network resulting from the graph created in Fig. 4. Consider a simplified representation of the dataset, consisting of variants 1A, 1B, 1C, and 1D (located on chromosome 1) as well as variants 2A, 2B, 2C, and 2D (located on chromosome 2). Source and sink nodes are added to the graph, and the variants in each fold are duplicated to constrain flow through the network. (Lower) A potential result of running the Ford-Fulkerson algorithm is shown here. The value of the maximum total flow through the graph is 2, as shown by the two paths (highlighted in red) that connect the source to the sink. We see from the graph that variants 1A, 1B, and 1C are part of a region of the genome that remains stable across multiple folds; this property can be noted for variants 2A, 2B, and 2C as well

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BioData Mining

ISSN: 1756-0381