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Two different models of inferring binary interactions from the lists of identifications have been widely used the spoke and matrix models [22].
The 'interaction cardinality matching' parameter specifies whether the binary interactions from the query network should be matched only with binary interactions from the database network (strict matching) or whether they may be matched to complex interactions, i.e. interactions of more than two proteins that contain the binary interactions.
A high confidence dataset of 27 286 binary interactions involving 9596 proteins was built by joining (i) 2325 human interactions manually extracted from the literature and (ii) 24 961 binary interactions from the APID database [ Prieto and De Las Rivas (2006), http://bioinfow.dep.usal.es/apid/].
The CORUM dataset used in this work was generated by overlaying multi-protein complexes described in the CORUM database with the binary interactions from the iRefIndex resource, while the HeLa dataset was derived by its authors by applying the CLusterOne method to a high confidence PPI network.
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The problem of identifying binary interactions from AP-MS data is then converted to the estimation of W dir given W obs. Once the estimator of W dir is obtained, we can predict binary interactions according to the value of this estimator (a higher value indicates the corresponding two proteins are more likely to physically interact with each other).
As one of the primary goals of the database is to clearly distinguish binary interactions from co-complex associations, two separate and mutually exclusive lists of evidence codes were created – one for each category.
The procedure of identifying binary interactions from co-complex interaction network using BINM is presented in Algorithm 1.
BINM is a post-processing scoring scheme that is devised to identify binary interactions from co-complex interactions.
These three sets of predictions were then compared against a set of high-quality binary interactions from G Yu.
In this study, a new scoring method is developed to identify binary interactions from AP-MS data.
These interactions contain binary interactions from two directions, from miRNAs to mRNAs coding for proteins, and from proteins to miRNA promoters and enhancers.
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