Progressive iterative signature algorithm (PISA) is another interesting approach, where larger functional modules are iteratively identified and removed to find more subtle functional modules (Kloster et al., 2005).
Data were iteratively examined to identify additional themes.
As errors or programming bugs were identified, class definitions were iteratively refined.
The basic principle behind modular decomposition is to iteratively identify and contract nodes that are in a certain sense equivalent, until no more equivalent nodes can be found in the graph.
Proteins identified in this analysis were iteratively compared by BLAST against the Bc species-group until no additional prospective sigma factors were found.
The data were iteratively analysed in regular research team meetings in order to identify emergent themes, to clarify inconsistencies or unusual findings.
Syndromes were iteratively reviewed and refined until additional exclusion criteria were determined to no longer make substantial impact on the number of cases identified.
Profiles were iteratively improved.
Interviews were iteratively read until thematic saturation was reached.
Conclusions derived from independent examination were iteratively compared and discussed.
