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A H Tilstra et al. presented the high-definition design structure matrix (HDDSM) to captures a spectrum of interactions between components of a product [22].
Each species of pathogen has a characteristic spectrum of interactions with its human hosts.
Apparently, SAP PDZ domains are open to a much larger spectrum of interactions than that encoded in the genomic sequences.
The redox properties of the central iron atom and the spectrum of interactions are provided by numerous variable protein scaffolds [43].
Other coculture models allow the study of a wider spectrum of interactions at the feto maternal interface.
Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions.
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More specifically, this analysis is not meant to uncovering the full spectrum of interaction patterns among mutations.
What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means.
Genes that function in the same pathways show consistent, suppressive genetic interactions across a spectrum of interaction partners.
Quantitative bioactivity assays provide convenient data that capture the whole spectrum of interaction affinities, including both true-positive and -negative interactions.
We argue that the experimental data sets of Kd/Ki or other bioactivity measurements provide more realistic response variable for the prediction problem in terms of representing the whole spectrum of interaction affinities, including both true-positive and -negative interactions.
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