By comparing the enrichment between Figures 2A and 2B, it is apparent that extreme S-scores could indicate co-functional membership more efficiently.
To accomplish this, we first quantitatively measure whether genetic interactions and co-expression indicate co-functional membership and, if so, to what degree.
In fact, early attempts to deduce gene functionality (that is, functional module membership) from gene co-expression revealed that many functional modules did not even show a detectable degree of internal co-expression [ 17, 18].
Nevertheless, the functional class membership of one protein may influence the class membership of a related protein.
These soft links could be dynamically established using property/ies such as homology, structural, functional similarities, membership to a certain biological process etc.
We used Ingenuity Pathways Analysis (IPA) software (Ingenuity® Systems, Redwood City, CA, USA) to assess functional class membership for probes identified as differentially expressed between probands and unrelated, matched controls.
In this algorithm, two kinds of local spatial attractions are considered in the functional form of membership connectedness and the required seeds can be selected automatically.
In this paper, after proving that solving the entailment problem for NDs using the chase procedure has exponential space complexity, we show that, given a set of inference rules similar to those used for functional dependencies, the membership problem for NDs is NP-hard.
The functional significance of membership in either or both of these groups is unclear.
The evidence used to define gene similarity includes text mining, pathway membership, functional annotations, protein properties, sequence, co-expression and proximity in protein protein interaction (PPI) networks.
