01946nas a2200157 4500008004100000245007200041210006900113260001200182300001400194490000700208520144400215100002101659700001801680700002001698856007001718 2018 eng d00aIndeCut evaluates performance of network motif discovery algorithms0 aIndeCut evaluates performance of network motif discovery algorit c05/2018 a1514-15210 v343 a
Motivation
Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets—thus it was not possible to assess the validity of resulting network motifs.
 
Results
In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.
1 aAnsariola, Mitra1 aMegraw, Molly1 aKoslicki, David uhttps://academic.oup.com/bioinformatics/article/34/9/1514/4721785