Our work provides an understanding into why novices can handle entity classes and identifying binary relationships with some ease, but find other components like ternary relationships difficult.
Therefore, besides bait-prey observations, prey-prey observations are also useful to identify binary interactions.
Furthermore, Y2H does typically not provide information on the motifs involved in the identified binary interactions.
Many of them only identify binary rather than quantitative variation, which are sensitive to threshold choices.
BINM is a post-processing scoring scheme that is devised to identify binary interactions from co-complex interactions.
The procedure of identifying binary interactions from co-complex interaction network using BINM is presented in Algorithm 1.
In this study, a new scoring method is developed to identify binary interactions from AP-MS data.
In this paper, we have presented a new network topology-based method to identify binary interactions in co-complex interaction network which is induced from AP-MS data.
Conventional approaches use peak callers (6) to identify binary binding events in each cell line separately and then take the difference between cell lines.
Although AP-MS is not designed to identify binary interactions, based on the data it generates, we could distinguish direct interactions from indirect interactions by analyzing the topological structure of AP-MS data [ 12, 20– 20].
