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"Dracula" has what Noël Carroll, in "The Philosophy of Horror" (1990), called a "complex discovery plot"—that is, a plot that involves not just the discovery of an evil force let loose in the world but the job of convincing skeptics (which takes a lot of time, allowing the monster to compound his crimes) that such a thing is happening.
"Dracula" has what Noël Carroll, in "The Philosophy of Horror" (1990), called a "complex discovery plot" — that is, a plot that involves not just the discovery of an evil force let loose in the world but the job of convincing skeptics (which takes a lot of time, allowing the monster to compound his crimes) that such a thing is happening.
However, it is not an exceptionally useful imagery when it comes to conceptualizing the processes behind the achievement of a complex discovery through effortful thinking generally.
From this perspective, the protein complex discovery methods based on static PPI networks have limitations.
Thus the discovery of small complexes is an important subtask within complex discovery.
More protein complex discovery algorithms can be referred to in [ 16, 17].
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Quintard, A. & Rodriguez, J. Iron cyclopentadienone complexes: discovery, properties, and catalytic reactivity.
We have revised the Background section to include a brief mention of some popular complex-discovery algorithms.
Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes.
By splitting dense regions of the PPI network into less-dense but coherent subnetworks, complex-discovery performance is improved, with the biggest improvements among complexes embedded in highly-connected regions.
Note that the original PPI decomposition approach (as described in [ 10]) did not aggregate clusters from multiple clustering algorithms, but used individual clustering algorithms instead; here we incorporate the COMBINE strategy for DECOMP as it was found to improve complex-discovery performance in [ 9].
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